Overview

Brought to you by YData

Dataset statistics

Number of variables55
Number of observations999
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.3 MiB
Average record size in memory4.4 KiB

Variable types

Text23
Numeric1
Categorical31

Alerts

Authority_Present is highly overall correlated with Clarity_and_Conciseness_Value and 30 other fieldsHigh correlation
Clarity_and_Conciseness_Value is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Contains_Colon is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Contains_Exclamation_Mark is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Contains_Hyphen is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Contains_Numbers is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Contains_Question_Mark is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Contains_Quotes is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Curiosity_Present is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Economic_Benefit_Present is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Emphatic_Capitalization_Usage is highly overall correlated with Authority_Present and 27 other fieldsHigh correlation
Ends_With_Question_Mark is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Exclusivity_Present is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Exclusivity_Words is highly overall correlated with Authority_Present and 27 other fieldsHigh correlation
Fear_Concern_Present is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Hope_Optimism_Present is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Indignation_Controversy_Present is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Length_General_Assessment is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Main_Category is highly overall correlated with Authority_Present and 27 other fieldsHigh correlation
Main_Classification is highly overall correlated with Authority_Present and 27 other fieldsHigh correlation
National_Relevance_Present is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Originality_and_Differentiation_Value is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Personal_Identification_Present is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Prohibition_Restriction_Present is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Recognized_Brand_Present is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Relevance_and_Timeliness_Value is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Solution_Present is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Starts_With_Number is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Strategic_Keyword_Usage_Value is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Surprise_Awe_Present is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Temporal_Urgency_Present is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Visibility is highly overall correlated with Authority_Present and 30 other fieldsHigh correlation
Clarity_and_Conciseness_Value is highly imbalanced (85.7%) Imbalance
Relevance_and_Timeliness_Value is highly imbalanced (75.0%) Imbalance
Strategic_Keyword_Usage_Value is highly imbalanced (75.5%) Imbalance
Contains_Question_Mark is highly imbalanced (84.0%) Imbalance
Contains_Colon is highly imbalanced (71.3%) Imbalance
Contains_Exclamation_Mark is highly imbalanced (91.4%) Imbalance
Starts_With_Number is highly imbalanced (79.6%) Imbalance
Ends_With_Question_Mark is highly imbalanced (85.5%) Imbalance
Length_General_Assessment is highly imbalanced (80.6%) Imbalance
Emphatic_Capitalization_Usage is highly imbalanced (91.1%) Imbalance
Main_Classification is highly imbalanced (50.6%) Imbalance
Temporal_Urgency_Present is highly imbalanced (53.1%) Imbalance
Exclusivity_Present is highly imbalanced (76.9%) Imbalance
Exclusivity_Words is highly imbalanced (89.0%) Imbalance
Solution_Present is highly imbalanced (56.0%) Imbalance
Prohibition_Restriction_Present is highly imbalanced (64.5%) Imbalance
Visibility has unique values Unique

Reproduction

Analysis started2025-07-07 08:48:30.495794
Analysis finished2025-07-07 08:48:48.965837
Duration18.47 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Title
Text

Distinct983
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size157.4 KiB
2025-07-07T08:48:49.356899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length189
Median length109
Mean length73.832833
Min length18

Characters and Unicode

Total characters73759
Distinct characters88
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique967 ?
Unique (%)96.8%

Sample

1st rowBritish jets intercept 15 Russian military aircraft
2nd rowNew driveway rule change affecting every home in England has begun
3rd rowThe historic English city that is the best day trip within one hour of London
4th rowDavid Mitchell convicted for covering road with potatoes and silt
5th rowSay goodbye to parasols – Tesco is selling a smarter option that covers your entire outdoor space better
ValueCountFrequency (%)
to 429
 
3.4%
in 268
 
2.1%
the 210
 
1.7%
and 199
 
1.6%
of 197
 
1.6%
for 194
 
1.6%
with 138
 
1.1%
uk 133
 
1.1%
as 131
 
1.1%
on 120
 
1.0%
Other values (3420) 10454
83.8%
2025-07-07T08:48:49.929127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11474
15.6%
e 6781
 
9.2%
a 4706
 
6.4%
t 4589
 
6.2%
n 4361
 
5.9%
s 4321
 
5.9%
o 4310
 
5.8%
r 4187
 
5.7%
i 3887
 
5.3%
l 2625
 
3.6%
Other values (78) 22518
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 73759
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11474
15.6%
e 6781
 
9.2%
a 4706
 
6.4%
t 4589
 
6.2%
n 4361
 
5.9%
s 4321
 
5.9%
o 4310
 
5.8%
r 4187
 
5.7%
i 3887
 
5.3%
l 2625
 
3.6%
Other values (78) 22518
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 73759
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11474
15.6%
e 6781
 
9.2%
a 4706
 
6.4%
t 4589
 
6.2%
n 4361
 
5.9%
s 4321
 
5.9%
o 4310
 
5.8%
r 4187
 
5.7%
i 3887
 
5.3%
l 2625
 
3.6%
Other values (78) 22518
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 73759
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11474
15.6%
e 6781
 
9.2%
a 4706
 
6.4%
t 4589
 
6.2%
n 4361
 
5.9%
s 4321
 
5.9%
o 4310
 
5.8%
r 4187
 
5.7%
i 3887
 
5.3%
l 2625
 
3.6%
Other values (78) 22518
30.5%

Visibility
Real number (ℝ)

High correlation  Unique 

Distinct999
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1331401.1
Minimum316132
Maximum10893901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2025-07-07T08:48:50.089954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum316132
5-th percentile339486.6
Q1456888.5
median752044
Q31512781
95-th percentile4327268.2
Maximum10893901
Range10577769
Interquartile range (IQR)1055892.5

Descriptive statistics

Standard deviation1526279.8
Coefficient of variation (CV)1.1463711
Kurtosis11.171185
Mean1331401.1
Median Absolute Deviation (MAD)350735
Skewness3.0255473
Sum1.3300697 × 109
Variance2.3295299 × 1012
MonotonicityStrictly decreasing
2025-07-07T08:48:50.234853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
316132 1
 
0.1%
10893901 1
 
0.1%
10439139 1
 
0.1%
10098169 1
 
0.1%
10031250 1
 
0.1%
9765281 1
 
0.1%
9557677 1
 
0.1%
324058 1
 
0.1%
324091 1
 
0.1%
325259 1
 
0.1%
Other values (989) 989
99.0%
ValueCountFrequency (%)
316132 1
0.1%
316533 1
0.1%
317591 1
0.1%
317685 1
0.1%
317796 1
0.1%
318074 1
0.1%
318561 1
0.1%
319337 1
0.1%
319502 1
0.1%
320207 1
0.1%
ValueCountFrequency (%)
10893901 1
0.1%
10439139 1
0.1%
10098169 1
0.1%
10031250 1
0.1%
9765281 1
0.1%
9557677 1
0.1%
9470424 1
0.1%
9365831 1
0.1%
9220647 1
0.1%
9175453 1
0.1%
Distinct982
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size155.0 KiB
2025-07-07T08:48:50.602331image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length189
Median length109
Mean length72.362362
Min length0

Characters and Unicode

Total characters72290
Distinct characters86
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique980 ?
Unique (%)98.1%

Sample

1st rowBritish jets intercept 15 Russian military aircraft
2nd rowNew driveway rule change affecting every home in England has begun
3rd rowThe historic English city that is the best day trip within one hour of London
4th rowDavid Mitchell convicted for covering road with potatoes and silt
5th rowSay goodbye to parasols – Tesco is selling a smarter option that covers your entire outdoor space better
ValueCountFrequency (%)
to 424
 
3.5%
in 263
 
2.2%
the 209
 
1.7%
of 196
 
1.6%
and 194
 
1.6%
for 185
 
1.5%
with 135
 
1.1%
uk 131
 
1.1%
as 127
 
1.0%
on 118
 
1.0%
Other values (3411) 10248
83.8%
2025-07-07T08:48:51.359288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11248
15.6%
e 6652
 
9.2%
a 4615
 
6.4%
t 4513
 
6.2%
n 4268
 
5.9%
o 4238
 
5.9%
s 4224
 
5.8%
r 4113
 
5.7%
i 3826
 
5.3%
l 2576
 
3.6%
Other values (76) 22017
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 72290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11248
15.6%
e 6652
 
9.2%
a 4615
 
6.4%
t 4513
 
6.2%
n 4268
 
5.9%
o 4238
 
5.9%
s 4224
 
5.8%
r 4113
 
5.7%
i 3826
 
5.3%
l 2576
 
3.6%
Other values (76) 22017
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 72290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11248
15.6%
e 6652
 
9.2%
a 4615
 
6.4%
t 4513
 
6.2%
n 4268
 
5.9%
o 4238
 
5.9%
s 4224
 
5.8%
r 4113
 
5.7%
i 3826
 
5.3%
l 2576
 
3.6%
Other values (76) 22017
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 72290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11248
15.6%
e 6652
 
9.2%
a 4615
 
6.4%
t 4513
 
6.2%
n 4268
 
5.9%
o 4238
 
5.9%
s 4224
 
5.8%
r 4113
 
5.7%
i 3826
 
5.3%
l 2576
 
3.6%
Other values (76) 22017
30.5%

Main_Category
Categorical

High correlation 

Distinct17
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size73.1 KiB
News_and_Current_Events
306 
Finance_and_Business
176 
Travel
95 
Entertainment_and_Culture
92 
Home_and_Lifestyle
75 
Other values (12)
255 

Length

Max length26
Median length23
Mean length17.796797
Min length0

Characters and Unicode

Total characters17779
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.2%

Sample

1st rowNews_and_Current_Events
2nd rowNews_and_Current_Events
3rd rowTravel
4th rowNews_and_Current_Events
5th rowHome_and_Lifestyle

Common Values

ValueCountFrequency (%)
News_and_Current_Events 306
30.6%
Finance_and_Business 176
17.6%
Travel 95
 
9.5%
Entertainment_and_Culture 92
 
9.2%
Home_and_Lifestyle 75
 
7.5%
Public_Safety 47
 
4.7%
Sports 40
 
4.0%
Health_and_Wellness 33
 
3.3%
Science 30
 
3.0%
Gastronomy 25
 
2.5%
Other values (7) 80
 
8.0%

Length

2025-07-07T08:48:51.542573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
news_and_current_events 306
31.2%
finance_and_business 176
17.9%
travel 95
 
9.7%
entertainment_and_culture 92
 
9.4%
home_and_lifestyle 75
 
7.6%
public_safety 47
 
4.8%
sports 40
 
4.1%
health_and_wellness 33
 
3.4%
science 30
 
3.1%
gastronomy 25
 
2.5%
Other values (6) 63
 
6.4%

Most occurring characters

ValueCountFrequency (%)
n 2284
12.8%
e 2145
12.1%
_ 1785
10.0%
s 1439
 
8.1%
t 1234
 
6.9%
a 1232
 
6.9%
r 980
 
5.5%
u 758
 
4.3%
d 738
 
4.2%
i 725
 
4.1%
Other values (26) 4459
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17779
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2284
12.8%
e 2145
12.1%
_ 1785
10.0%
s 1439
 
8.1%
t 1234
 
6.9%
a 1232
 
6.9%
r 980
 
5.5%
u 758
 
4.3%
d 738
 
4.2%
i 725
 
4.1%
Other values (26) 4459
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17779
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2284
12.8%
e 2145
12.1%
_ 1785
10.0%
s 1439
 
8.1%
t 1234
 
6.9%
a 1232
 
6.9%
r 980
 
5.5%
u 758
 
4.3%
d 738
 
4.2%
i 725
 
4.1%
Other values (26) 4459
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17779
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2284
12.8%
e 2145
12.1%
_ 1785
10.0%
s 1439
 
8.1%
t 1234
 
6.9%
a 1232
 
6.9%
r 980
 
5.5%
u 758
 
4.3%
d 738
 
4.2%
i 725
 
4.1%
Other values (26) 4459
25.1%
Distinct106
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Memory size70.8 KiB
2025-07-07T08:48:51.848632image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length33
Median length26
Mean length15.439439
Min length0

Characters and Unicode

Total characters15424
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)4.9%

Sample

1st rowInternational
2nd rowGovernment
3rd rowDestinations
4th rowCrime & Judicial
5th rowGardening_and_Plants
ValueCountFrequency (%)
320
 
17.5%
personal 92
 
5.0%
finance 92
 
5.0%
politics 85
 
4.7%
destinations 62
 
3.4%
international 62
 
3.4%
environment 59
 
3.2%
celebrities 59
 
3.2%
influencers 59
 
3.2%
prevention 53
 
2.9%
Other values (130) 882
48.3%
2025-07-07T08:48:52.380546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 1743
 
11.3%
e 1560
 
10.1%
i 1397
 
9.1%
r 987
 
6.4%
s 973
 
6.3%
a 971
 
6.3%
t 958
 
6.2%
843
 
5.5%
o 818
 
5.3%
l 765
 
5.0%
Other values (44) 4409
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1743
 
11.3%
e 1560
 
10.1%
i 1397
 
9.1%
r 987
 
6.4%
s 973
 
6.3%
a 971
 
6.3%
t 958
 
6.2%
843
 
5.5%
o 818
 
5.3%
l 765
 
5.0%
Other values (44) 4409
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1743
 
11.3%
e 1560
 
10.1%
i 1397
 
9.1%
r 987
 
6.4%
s 973
 
6.3%
a 971
 
6.3%
t 958
 
6.2%
843
 
5.5%
o 818
 
5.3%
l 765
 
5.0%
Other values (44) 4409
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1743
 
11.3%
e 1560
 
10.1%
i 1397
 
9.1%
r 987
 
6.4%
s 973
 
6.3%
a 971
 
6.3%
t 958
 
6.2%
843
 
5.5%
o 818
 
5.3%
l 765
 
5.0%
Other values (44) 4409
28.6%
Distinct113
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Memory size61.0 KiB
2025-07-07T08:48:52.755105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length3
Mean length5.3683684
Min length0

Characters and Unicode

Total characters5363
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)7.8%

Sample

1st rowN/A
2nd rowN/A
3rd rowGuides
4th rowN/A
5th rowN/A
ValueCountFrequency (%)
n/a 638
59.4%
government 62
 
5.8%
tips 43
 
4.0%
national 32
 
3.0%
guides 26
 
2.4%
19
 
1.8%
international 16
 
1.5%
prevention 11
 
1.0%
transfers 9
 
0.8%
main 7
 
0.7%
Other values (125) 211
 
19.6%
2025-07-07T08:48:53.313681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 676
12.6%
A 660
12.3%
/ 639
11.9%
e 422
 
7.9%
n 342
 
6.4%
i 283
 
5.3%
t 260
 
4.8%
s 256
 
4.8%
r 215
 
4.0%
a 215
 
4.0%
Other values (39) 1395
26.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5363
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 676
12.6%
A 660
12.3%
/ 639
11.9%
e 422
 
7.9%
n 342
 
6.4%
i 283
 
5.3%
t 260
 
4.8%
s 256
 
4.8%
r 215
 
4.0%
a 215
 
4.0%
Other values (39) 1395
26.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5363
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 676
12.6%
A 660
12.3%
/ 639
11.9%
e 422
 
7.9%
n 342
 
6.4%
i 283
 
5.3%
t 260
 
4.8%
s 256
 
4.8%
r 215
 
4.0%
a 215
 
4.0%
Other values (39) 1395
26.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5363
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 676
12.6%
A 660
12.3%
/ 639
11.9%
e 422
 
7.9%
n 342
 
6.4%
i 283
 
5.3%
t 260
 
4.8%
s 256
 
4.8%
r 215
 
4.0%
a 215
 
4.0%
Other values (39) 1395
26.0%

Clarity_and_Conciseness_Value
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size59.6 KiB
High
960 
Medium
 
20
 
17
Low
 
2

Length

Max length6
Median length4
Mean length3.96997
Min length0

Characters and Unicode

Total characters3966
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowHigh
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
High 960
96.1%
Medium 20
 
2.0%
17
 
1.7%
Low 2
 
0.2%

Length

2025-07-07T08:48:53.438119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:48:53.527186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high 960
97.8%
medium 20
 
2.0%
low 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i 980
24.7%
H 960
24.2%
g 960
24.2%
h 960
24.2%
M 20
 
0.5%
e 20
 
0.5%
d 20
 
0.5%
u 20
 
0.5%
m 20
 
0.5%
L 2
 
0.1%
Other values (2) 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3966
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 980
24.7%
H 960
24.2%
g 960
24.2%
h 960
24.2%
M 20
 
0.5%
e 20
 
0.5%
d 20
 
0.5%
u 20
 
0.5%
m 20
 
0.5%
L 2
 
0.1%
Other values (2) 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3966
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 980
24.7%
H 960
24.2%
g 960
24.2%
h 960
24.2%
M 20
 
0.5%
e 20
 
0.5%
d 20
 
0.5%
u 20
 
0.5%
m 20
 
0.5%
L 2
 
0.1%
Other values (2) 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3966
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 980
24.7%
H 960
24.2%
g 960
24.2%
h 960
24.2%
M 20
 
0.5%
e 20
 
0.5%
d 20
 
0.5%
u 20
 
0.5%
m 20
 
0.5%
L 2
 
0.1%
Other values (2) 4
 
0.1%
Distinct804
Distinct (%)80.5%
Missing0
Missing (%)0.0%
Memory size140.9 KiB
2025-07-07T08:48:53.837453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length222
Median length145
Mean length86.192192
Min length0

Characters and Unicode

Total characters86106
Distinct characters73
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique770 ?
Unique (%)77.1%

Sample

1st rowThe headline is direct and clearly states the event without ambiguity.
2nd rowThe headline is direct and easy to understand, clearly stating the what, who, and where.
3rd rowThe main message is very clear and easy to understand, directly stating the topic.
4th rowThe headline clearly states who was convicted and for what unusual reason.
5th rowThe main message is straightforward and easy to grasp.
ValueCountFrequency (%)
the 1639
 
11.7%
and 1168
 
8.4%
is 884
 
6.3%
to 625
 
4.5%
message 608
 
4.3%
main 565
 
4.0%
easy 541
 
3.9%
understand 533
 
3.8%
clear 530
 
3.8%
headline 345
 
2.5%
Other values (1433) 6540
46.8%
2025-07-07T08:48:54.337092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12996
15.1%
e 9584
11.1%
a 7519
 
8.7%
t 5871
 
6.8%
n 5719
 
6.6%
s 5674
 
6.6%
i 5319
 
6.2%
d 3861
 
4.5%
r 3757
 
4.4%
o 2843
 
3.3%
Other values (63) 22963
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 86106
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12996
15.1%
e 9584
11.1%
a 7519
 
8.7%
t 5871
 
6.8%
n 5719
 
6.6%
s 5674
 
6.6%
i 5319
 
6.2%
d 3861
 
4.5%
r 3757
 
4.4%
o 2843
 
3.3%
Other values (63) 22963
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 86106
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12996
15.1%
e 9584
11.1%
a 7519
 
8.7%
t 5871
 
6.8%
n 5719
 
6.6%
s 5674
 
6.6%
i 5319
 
6.2%
d 3861
 
4.5%
r 3757
 
4.4%
o 2843
 
3.3%
Other values (63) 22963
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 86106
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12996
15.1%
e 9584
11.1%
a 7519
 
8.7%
t 5871
 
6.8%
n 5719
 
6.6%
s 5674
 
6.6%
i 5319
 
6.2%
d 3861
 
4.5%
r 3757
 
4.4%
o 2843
 
3.3%
Other values (63) 22963
26.7%

Relevance_and_Timeliness_Value
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size59.7 KiB
High
915 
Medium
 
63
 
17
Low
 
4

Length

Max length6
Median length4
Mean length4.0540541
Min length0

Characters and Unicode

Total characters4050
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowHigh
4th rowLow
5th rowHigh

Common Values

ValueCountFrequency (%)
High 915
91.6%
Medium 63
 
6.3%
17
 
1.7%
Low 4
 
0.4%

Length

2025-07-07T08:48:54.454971image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:48:54.520815image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high 915
93.2%
medium 63
 
6.4%
low 4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
i 978
24.1%
H 915
22.6%
g 915
22.6%
h 915
22.6%
M 63
 
1.6%
e 63
 
1.6%
d 63
 
1.6%
u 63
 
1.6%
m 63
 
1.6%
L 4
 
0.1%
Other values (2) 8
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4050
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 978
24.1%
H 915
22.6%
g 915
22.6%
h 915
22.6%
M 63
 
1.6%
e 63
 
1.6%
d 63
 
1.6%
u 63
 
1.6%
m 63
 
1.6%
L 4
 
0.1%
Other values (2) 8
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4050
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 978
24.1%
H 915
22.6%
g 915
22.6%
h 915
22.6%
M 63
 
1.6%
e 63
 
1.6%
d 63
 
1.6%
u 63
 
1.6%
m 63
 
1.6%
L 4
 
0.1%
Other values (2) 8
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4050
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 978
24.1%
H 915
22.6%
g 915
22.6%
h 915
22.6%
M 63
 
1.6%
e 63
 
1.6%
d 63
 
1.6%
u 63
 
1.6%
m 63
 
1.6%
L 4
 
0.1%
Other values (2) 8
 
0.2%
Distinct983
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size162.8 KiB
2025-07-07T08:48:54.830372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length237
Median length155
Mean length109.7047
Min length0

Characters and Unicode

Total characters109595
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique982 ?
Unique (%)98.3%

Sample

1st rowCovers a geopolitically relevant and timely topic concerning international military activities.
2nd rowThe use of 'New' and 'has begun' creates a sense of immediacy and relevance for the target audience.
3rd rowDay trips and proximity to major cities like London are evergreen topics with high interest.
4th rowThe event is localized and not tied to a major ongoing news trend, making its general relevance low.
5th rowThe topic of outdoor space solutions is relevant, especially during warmer seasons, and appeals to practical home improvement interests.
ValueCountFrequency (%)
and 1109
 
6.7%
a 765
 
4.7%
relevant 615
 
3.7%
the 567
 
3.4%
of 503
 
3.1%
to 493
 
3.0%
highly 465
 
2.8%
is 369
 
2.2%
interest 347
 
2.1%
are 335
 
2.0%
Other values (1767) 10875
66.1%
2025-07-07T08:48:55.330990image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15461
14.1%
e 12197
11.1%
i 8018
 
7.3%
n 7943
 
7.2%
a 7725
 
7.0%
t 7283
 
6.6%
r 5836
 
5.3%
s 5494
 
5.0%
o 4999
 
4.6%
l 4837
 
4.4%
Other values (58) 29802
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 109595
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15461
14.1%
e 12197
11.1%
i 8018
 
7.3%
n 7943
 
7.2%
a 7725
 
7.0%
t 7283
 
6.6%
r 5836
 
5.3%
s 5494
 
5.0%
o 4999
 
4.6%
l 4837
 
4.4%
Other values (58) 29802
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 109595
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15461
14.1%
e 12197
11.1%
i 8018
 
7.3%
n 7943
 
7.2%
a 7725
 
7.0%
t 7283
 
6.6%
r 5836
 
5.3%
s 5494
 
5.0%
o 4999
 
4.6%
l 4837
 
4.4%
Other values (58) 29802
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 109595
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15461
14.1%
e 12197
11.1%
i 8018
 
7.3%
n 7943
 
7.2%
a 7725
 
7.0%
t 7283
 
6.6%
r 5836
 
5.3%
s 5494
 
5.0%
o 4999
 
4.6%
l 4837
 
4.4%
Other values (58) 29802
27.2%

Strategic_Keyword_Usage_Value
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size59.7 KiB
High
915 
Medium
 
65
 
17
Low
 
2

Length

Max length6
Median length4
Mean length4.0600601
Min length0

Characters and Unicode

Total characters4056
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigh
2nd rowHigh
3rd rowHigh
4th rowLow
5th rowHigh

Common Values

ValueCountFrequency (%)
High 915
91.6%
Medium 65
 
6.5%
17
 
1.7%
Low 2
 
0.2%

Length

2025-07-07T08:48:55.450658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:48:55.520699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high 915
93.2%
medium 65
 
6.6%
low 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i 980
24.2%
H 915
22.6%
g 915
22.6%
h 915
22.6%
M 65
 
1.6%
e 65
 
1.6%
d 65
 
1.6%
u 65
 
1.6%
m 65
 
1.6%
L 2
 
< 0.1%
Other values (2) 4
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 980
24.2%
H 915
22.6%
g 915
22.6%
h 915
22.6%
M 65
 
1.6%
e 65
 
1.6%
d 65
 
1.6%
u 65
 
1.6%
m 65
 
1.6%
L 2
 
< 0.1%
Other values (2) 4
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 980
24.2%
H 915
22.6%
g 915
22.6%
h 915
22.6%
M 65
 
1.6%
e 65
 
1.6%
d 65
 
1.6%
u 65
 
1.6%
m 65
 
1.6%
L 2
 
< 0.1%
Other values (2) 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 980
24.2%
H 915
22.6%
g 915
22.6%
h 915
22.6%
M 65
 
1.6%
e 65
 
1.6%
d 65
 
1.6%
u 65
 
1.6%
m 65
 
1.6%
L 2
 
< 0.1%
Other values (2) 4
 
0.1%
Distinct983
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size188.4 KiB
2025-07-07T08:48:55.764125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length244
Median length170
Mean length123.0991
Min length0

Characters and Unicode

Total characters122976
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique982 ?
Unique (%)98.3%

Sample

1st rowUses clear, descriptive keywords like 'British jets', 'intercept', 'Russian military aircraft' which are highly relevant to the topic and discoverable.
2nd rowUses specific and relevant keywords like 'driveway rule change' and 'England' that directly target a specific audience's potential interests.
3rd rowUses highly relevant keywords like "historic English city," "best day trip," and "one hour of London" which are likely search terms.
4th rowThe keywords are too specific to the event and do not align with common search queries.
5th rowKeywords like "parasols", "Tesco", and "outdoor space" are relevant to the topic and target audience.
ValueCountFrequency (%)
and 1547
 
8.8%
relevant 779
 
4.4%
keywords 767
 
4.3%
like 748
 
4.2%
are 731
 
4.1%
highly 520
 
2.9%
uses 519
 
2.9%
to 463
 
2.6%
the 435
 
2.5%
audience 329
 
1.9%
Other values (2593) 10831
61.3%
2025-07-07T08:48:56.202852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16691
13.6%
e 13262
 
10.8%
a 8660
 
7.0%
r 7072
 
5.8%
t 6826
 
5.6%
n 6727
 
5.5%
s 6392
 
5.2%
i 6164
 
5.0%
l 5113
 
4.2%
o 4675
 
3.8%
Other values (70) 41394
33.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 122976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16691
13.6%
e 13262
 
10.8%
a 8660
 
7.0%
r 7072
 
5.8%
t 6826
 
5.6%
n 6727
 
5.5%
s 6392
 
5.2%
i 6164
 
5.0%
l 5113
 
4.2%
o 4675
 
3.8%
Other values (70) 41394
33.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 122976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16691
13.6%
e 13262
 
10.8%
a 8660
 
7.0%
r 7072
 
5.8%
t 6826
 
5.6%
n 6727
 
5.5%
s 6392
 
5.2%
i 6164
 
5.0%
l 5113
 
4.2%
o 4675
 
3.8%
Other values (70) 41394
33.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 122976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16691
13.6%
e 13262
 
10.8%
a 8660
 
7.0%
r 7072
 
5.8%
t 6826
 
5.6%
n 6727
 
5.5%
s 6392
 
5.2%
i 6164
 
5.0%
l 5113
 
4.2%
o 4675
 
3.8%
Other values (70) 41394
33.7%

Originality_and_Differentiation_Value
Categorical

High correlation 

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size60.9 KiB
Medium
745 
High
156 
Low
81 
 
17

Length

Max length6
Median length6
Mean length5.3423423
Min length0

Characters and Unicode

Total characters5337
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowMedium
3rd rowMedium
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
Medium 745
74.6%
High 156
 
15.6%
Low 81
 
8.1%
17
 
1.7%

Length

2025-07-07T08:48:56.343658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:48:56.418568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
medium 745
75.9%
high 156
 
15.9%
low 81
 
8.2%

Most occurring characters

ValueCountFrequency (%)
i 901
16.9%
M 745
14.0%
e 745
14.0%
d 745
14.0%
u 745
14.0%
m 745
14.0%
H 156
 
2.9%
g 156
 
2.9%
h 156
 
2.9%
L 81
 
1.5%
Other values (2) 162
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 5337
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 901
16.9%
M 745
14.0%
e 745
14.0%
d 745
14.0%
u 745
14.0%
m 745
14.0%
H 156
 
2.9%
g 156
 
2.9%
h 156
 
2.9%
L 81
 
1.5%
Other values (2) 162
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 5337
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 901
16.9%
M 745
14.0%
e 745
14.0%
d 745
14.0%
u 745
14.0%
m 745
14.0%
H 156
 
2.9%
g 156
 
2.9%
h 156
 
2.9%
L 81
 
1.5%
Other values (2) 162
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 5337
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 901
16.9%
M 745
14.0%
e 745
14.0%
d 745
14.0%
u 745
14.0%
m 745
14.0%
H 156
 
2.9%
g 156
 
2.9%
h 156
 
2.9%
L 81
 
1.5%
Other values (2) 162
 
3.0%
Distinct983
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size182.4 KiB
2025-07-07T08:48:56.690633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length270
Median length175
Mean length124.00501
Min length0

Characters and Unicode

Total characters123881
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique982 ?
Unique (%)98.3%

Sample

1st rowWhile the event is specific, the phrasing is fairly standard for news reporting on military interceptions.
2nd rowWhile the topic is specific, the headline's structure is quite common for news alerts, lacking a highly unique angle.
3rd rowWhile "best day trip" is a common superlative, the specific geographic and historical context provides some differentiation.
4th rowThe bizarre nature of the crime (covering a road with potatoes and silt) makes the headline highly original and memorable.
5th rowThe headline positions the product as a "smarter option" and an improvement over traditional parasols, offering a unique angle.
ValueCountFrequency (%)
the 1599
 
8.2%
a 1131
 
5.8%
is 800
 
4.1%
and 598
 
3.1%
specific 543
 
2.8%
unique 528
 
2.7%
of 503
 
2.6%
common 493
 
2.5%
while 461
 
2.4%
angle 381
 
2.0%
Other values (2241) 12394
63.8%
2025-07-07T08:48:57.179519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18449
14.9%
e 12094
 
9.8%
i 9785
 
7.9%
t 8349
 
6.7%
n 8087
 
6.5%
a 7890
 
6.4%
o 6325
 
5.1%
s 6100
 
4.9%
r 5446
 
4.4%
c 4285
 
3.5%
Other values (72) 37071
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 123881
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
18449
14.9%
e 12094
 
9.8%
i 9785
 
7.9%
t 8349
 
6.7%
n 8087
 
6.5%
a 7890
 
6.4%
o 6325
 
5.1%
s 6100
 
4.9%
r 5446
 
4.4%
c 4285
 
3.5%
Other values (72) 37071
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 123881
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
18449
14.9%
e 12094
 
9.8%
i 9785
 
7.9%
t 8349
 
6.7%
n 8087
 
6.5%
a 7890
 
6.4%
o 6325
 
5.1%
s 6100
 
4.9%
r 5446
 
4.4%
c 4285
 
3.5%
Other values (72) 37071
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 123881
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
18449
14.9%
e 12094
 
9.8%
i 9785
 
7.9%
t 8349
 
6.7%
n 8087
 
6.5%
a 7890
 
6.4%
o 6325
 
5.1%
s 6100
 
4.9%
r 5446
 
4.4%
c 4285
 
3.5%
Other values (72) 37071
29.9%

Contains_Numbers
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.1 KiB
No
518 
Yes
464 
 
17

Length

Max length3
Median length2
Mean length2.4304304
Min length0

Characters and Unicode

Total characters2428
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowNo
3rd rowYes
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 518
51.9%
Yes 464
46.4%
17
 
1.7%

Length

2025-07-07T08:48:57.300018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:48:57.385415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 518
52.7%
yes 464
47.3%

Most occurring characters

ValueCountFrequency (%)
N 518
21.3%
o 518
21.3%
Y 464
19.1%
e 464
19.1%
s 464
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2428
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 518
21.3%
o 518
21.3%
Y 464
19.1%
e 464
19.1%
s 464
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2428
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 518
21.3%
o 518
21.3%
Y 464
19.1%
e 464
19.1%
s 464
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2428
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 518
21.3%
o 518
21.3%
Y 464
19.1%
e 464
19.1%
s 464
19.1%

Contains_Quotes
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
No
782 
Yes
200 
 
17

Length

Max length3
Median length2
Mean length2.1661662
Min length0

Characters and Unicode

Total characters2164
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 782
78.3%
Yes 200
 
20.0%
17
 
1.7%

Length

2025-07-07T08:48:57.469870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:48:57.539964image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 782
79.6%
yes 200
 
20.4%

Most occurring characters

ValueCountFrequency (%)
N 782
36.1%
o 782
36.1%
Y 200
 
9.2%
e 200
 
9.2%
s 200
 
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2164
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 782
36.1%
o 782
36.1%
Y 200
 
9.2%
e 200
 
9.2%
s 200
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2164
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 782
36.1%
o 782
36.1%
Y 200
 
9.2%
e 200
 
9.2%
s 200
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2164
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 782
36.1%
o 782
36.1%
Y 200
 
9.2%
e 200
 
9.2%
s 200
 
9.2%

Contains_Question_Mark
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
No
964 
Yes
 
18
 
17

Length

Max length3
Median length2
Mean length1.983984
Min length0

Characters and Unicode

Total characters1982
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 964
96.5%
Yes 18
 
1.8%
17
 
1.7%

Length

2025-07-07T08:48:57.625108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:48:57.697954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 964
98.2%
yes 18
 
1.8%

Most occurring characters

ValueCountFrequency (%)
N 964
48.6%
o 964
48.6%
Y 18
 
0.9%
e 18
 
0.9%
s 18
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 964
48.6%
o 964
48.6%
Y 18
 
0.9%
e 18
 
0.9%
s 18
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 964
48.6%
o 964
48.6%
Y 18
 
0.9%
e 18
 
0.9%
s 18
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 964
48.6%
o 964
48.6%
Y 18
 
0.9%
e 18
 
0.9%
s 18
 
0.9%

Contains_Colon
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
No
921 
Yes
 
61
 
17

Length

Max length3
Median length2
Mean length2.027027
Min length0

Characters and Unicode

Total characters2025
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 921
92.2%
Yes 61
 
6.1%
17
 
1.7%

Length

2025-07-07T08:48:57.787553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:48:57.857608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 921
93.8%
yes 61
 
6.2%

Most occurring characters

ValueCountFrequency (%)
N 921
45.5%
o 921
45.5%
Y 61
 
3.0%
e 61
 
3.0%
s 61
 
3.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2025
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 921
45.5%
o 921
45.5%
Y 61
 
3.0%
e 61
 
3.0%
s 61
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2025
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 921
45.5%
o 921
45.5%
Y 61
 
3.0%
e 61
 
3.0%
s 61
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2025
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 921
45.5%
o 921
45.5%
Y 61
 
3.0%
e 61
 
3.0%
s 61
 
3.0%

Contains_Hyphen
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
No
780 
Yes
202 
 
17

Length

Max length3
Median length2
Mean length2.1681682
Min length0

Characters and Unicode

Total characters2166
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowYes

Common Values

ValueCountFrequency (%)
No 780
78.1%
Yes 202
 
20.2%
17
 
1.7%

Length

2025-07-07T08:48:58.424526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:48:58.493798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 780
79.4%
yes 202
 
20.6%

Most occurring characters

ValueCountFrequency (%)
N 780
36.0%
o 780
36.0%
Y 202
 
9.3%
e 202
 
9.3%
s 202
 
9.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2166
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 780
36.0%
o 780
36.0%
Y 202
 
9.3%
e 202
 
9.3%
s 202
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2166
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 780
36.0%
o 780
36.0%
Y 202
 
9.3%
e 202
 
9.3%
s 202
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2166
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 780
36.0%
o 780
36.0%
Y 202
 
9.3%
e 202
 
9.3%
s 202
 
9.3%

Contains_Exclamation_Mark
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
No
981 
 
17
Yes
 
1

Length

Max length3
Median length2
Mean length1.966967
Min length0

Characters and Unicode

Total characters1965
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 981
98.2%
17
 
1.7%
Yes 1
 
0.1%

Length

2025-07-07T08:48:58.581219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:48:58.646427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 981
99.9%
yes 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
N 981
49.9%
o 981
49.9%
Y 1
 
0.1%
e 1
 
0.1%
s 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1965
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 981
49.9%
o 981
49.9%
Y 1
 
0.1%
e 1
 
0.1%
s 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1965
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 981
49.9%
o 981
49.9%
Y 1
 
0.1%
e 1
 
0.1%
s 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1965
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 981
49.9%
o 981
49.9%
Y 1
 
0.1%
e 1
 
0.1%
s 1
 
0.1%

Starts_With_Number
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
No
951 
Yes
 
31
 
17

Length

Max length3
Median length2
Mean length1.996997
Min length0

Characters and Unicode

Total characters1995
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 951
95.2%
Yes 31
 
3.1%
17
 
1.7%

Length

2025-07-07T08:48:58.736681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:48:58.813773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 951
96.8%
yes 31
 
3.2%

Most occurring characters

ValueCountFrequency (%)
N 951
47.7%
o 951
47.7%
Y 31
 
1.6%
e 31
 
1.6%
s 31
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1995
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 951
47.7%
o 951
47.7%
Y 31
 
1.6%
e 31
 
1.6%
s 31
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1995
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 951
47.7%
o 951
47.7%
Y 31
 
1.6%
e 31
 
1.6%
s 31
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1995
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 951
47.7%
o 951
47.7%
Y 31
 
1.6%
e 31
 
1.6%
s 31
 
1.6%

Ends_With_Question_Mark
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
No
968 
 
17
Yes
 
14

Length

Max length3
Median length2
Mean length1.97998
Min length0

Characters and Unicode

Total characters1978
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 968
96.9%
17
 
1.7%
Yes 14
 
1.4%

Length

2025-07-07T08:48:58.904878image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:48:58.985266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 968
98.6%
yes 14
 
1.4%

Most occurring characters

ValueCountFrequency (%)
N 968
48.9%
o 968
48.9%
Y 14
 
0.7%
e 14
 
0.7%
s 14
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1978
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 968
48.9%
o 968
48.9%
Y 14
 
0.7%
e 14
 
0.7%
s 14
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1978
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 968
48.9%
o 968
48.9%
Y 14
 
0.7%
e 14
 
0.7%
s 14
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1978
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 968
48.9%
o 968
48.9%
Y 14
 
0.7%
e 14
 
0.7%
s 14
 
0.7%

Length_General_Assessment
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size64.0 KiB
Adequate
954 
Too long, risk of truncation
 
28
 
17

Length

Max length28
Median length8
Mean length8.4244244
Min length0

Characters and Unicode

Total characters8416
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdequate
2nd rowAdequate
3rd rowAdequate
4th rowAdequate
5th rowAdequate

Common Values

ValueCountFrequency (%)
Adequate 954
95.5%
Too long, risk of truncation 28
 
2.8%
17
 
1.7%

Length

2025-07-07T08:48:59.068284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:48:59.134355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
adequate 954
87.2%
too 28
 
2.6%
long 28
 
2.6%
risk 28
 
2.6%
of 28
 
2.6%
truncation 28
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e 1908
22.7%
t 1010
12.0%
u 982
11.7%
a 982
11.7%
q 954
11.3%
d 954
11.3%
A 954
11.3%
o 140
 
1.7%
112
 
1.3%
n 84
 
1.0%
Other values (10) 336
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8416
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1908
22.7%
t 1010
12.0%
u 982
11.7%
a 982
11.7%
q 954
11.3%
d 954
11.3%
A 954
11.3%
o 140
 
1.7%
112
 
1.3%
n 84
 
1.0%
Other values (10) 336
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8416
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1908
22.7%
t 1010
12.0%
u 982
11.7%
a 982
11.7%
q 954
11.3%
d 954
11.3%
A 954
11.3%
o 140
 
1.7%
112
 
1.3%
n 84
 
1.0%
Other values (10) 336
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8416
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1908
22.7%
t 1010
12.0%
u 982
11.7%
a 982
11.7%
q 954
11.3%
d 954
11.3%
A 954
11.3%
o 140
 
1.7%
112
 
1.3%
n 84
 
1.0%
Other values (10) 336
 
4.0%
Distinct98
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
2025-07-07T08:48:59.382256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.013013
Min length0

Characters and Unicode

Total characters2011
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)1.9%

Sample

1st row46
2nd row68
3rd row69
4th row65
5th row84
ValueCountFrequency (%)
70 48
 
4.9%
69 46
 
4.7%
68 44
 
4.5%
66 42
 
4.3%
59 40
 
4.1%
75 39
 
4.0%
64 35
 
3.6%
60 34
 
3.5%
73 30
 
3.1%
50 26
 
2.6%
Other values (87) 598
60.9%
2025-07-07T08:48:59.792439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 378
18.8%
7 322
16.0%
5 241
12.0%
8 218
10.8%
9 213
10.6%
4 175
8.7%
0 169
8.4%
1 121
 
6.0%
3 98
 
4.9%
2 76
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2011
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6 378
18.8%
7 322
16.0%
5 241
12.0%
8 218
10.8%
9 213
10.6%
4 175
8.7%
0 169
8.4%
1 121
 
6.0%
3 98
 
4.9%
2 76
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2011
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6 378
18.8%
7 322
16.0%
5 241
12.0%
8 218
10.8%
9 213
10.6%
4 175
8.7%
0 169
8.4%
1 121
 
6.0%
3 98
 
4.9%
2 76
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2011
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6 378
18.8%
7 322
16.0%
5 241
12.0%
8 218
10.8%
9 213
10.6%
4 175
8.7%
0 169
8.4%
1 121
 
6.0%
3 98
 
4.9%
2 76
 
3.8%

Emphatic_Capitalization_Usage
Categorical

High correlation  Imbalance 

Distinct26
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size59.2 KiB
No
956 
 
17
Yes, 'RETURN', seeks impact
 
2
Yes, 'DWP', justified use
 
2
Yes, 'AI Cameras', 'Repeat Offenders', seeks impact
 
1
Other values (21)
 
21

Length

Max length151
Median length2
Mean length3.2222222
Min length0

Characters and Unicode

Total characters3219
Distinct characters52
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)2.2%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 956
95.7%
17
 
1.7%
Yes, 'RETURN', seeks impact 2
 
0.2%
Yes, 'DWP', justified use 2
 
0.2%
Yes, 'AI Cameras', 'Repeat Offenders', seeks impact 1
 
0.1%
Yes, 'Everything', seeks impact 1
 
0.1%
Yes, 'BBC', 'Strictly Come Dancing', justified use 1
 
0.1%
Yes, 'TODAY', justified use 1
 
0.1%
Yes, 'Essential Changes', seeks impact 1
 
0.1%
Yes, 'Say Goodbye To', seeks impact 1
 
0.1%
Other values (16) 16
 
1.6%

Length

2025-07-07T08:48:59.933949image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
no 956
83.0%
yes 26
 
2.3%
justified 13
 
1.1%
use 12
 
1.0%
impact 12
 
1.0%
seeks 11
 
1.0%
nouns 4
 
0.3%
and 4
 
0.3%
the 4
 
0.3%
proper 4
 
0.3%
Other values (87) 106
 
9.2%

Most occurring characters

ValueCountFrequency (%)
o 1001
31.1%
N 959
29.8%
170
 
5.3%
e 138
 
4.3%
s 107
 
3.3%
' 81
 
2.5%
i 77
 
2.4%
t 65
 
2.0%
, 64
 
2.0%
a 50
 
1.6%
Other values (42) 507
15.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3219
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1001
31.1%
N 959
29.8%
170
 
5.3%
e 138
 
4.3%
s 107
 
3.3%
' 81
 
2.5%
i 77
 
2.4%
t 65
 
2.0%
, 64
 
2.0%
a 50
 
1.6%
Other values (42) 507
15.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3219
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1001
31.1%
N 959
29.8%
170
 
5.3%
e 138
 
4.3%
s 107
 
3.3%
' 81
 
2.5%
i 77
 
2.4%
t 65
 
2.0%
, 64
 
2.0%
a 50
 
1.6%
Other values (42) 507
15.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3219
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1001
31.1%
N 959
29.8%
170
 
5.3%
e 138
 
4.3%
s 107
 
3.3%
' 81
 
2.5%
i 77
 
2.4%
t 65
 
2.0%
, 64
 
2.0%
a 50
 
1.6%
Other values (42) 507
15.8%

Main_Classification
Categorical

High correlation  Imbalance 

Distinct26
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size74.7 KiB
Declarative Simple
645 
Mystery/Revelation
66 
Attribution ('according to', 'reveals')
 
50
Superlative ('best', 'worst')
 
29
Direct Quote
 
27
Other values (21)
182 

Length

Max length52
Median length18
Mean length19.478478
Min length0

Characters and Unicode

Total characters19459
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.6%

Sample

1st rowDeclarative Simple
2nd rowDeclarative Simple
3rd rowSuperlative ('best', 'worst')
4th rowDeclarative Simple
5th rowComparative/Superlative

Common Values

ValueCountFrequency (%)
Declarative Simple 645
64.6%
Mystery/Revelation 66
 
6.6%
Attribution ('according to', 'reveals') 50
 
5.0%
Superlative ('best', 'worst') 29
 
2.9%
Direct Quote 27
 
2.7%
List/Numbered 26
 
2.6%
List/Numbered ('5 ways') 19
 
1.9%
17
 
1.7%
Direct Question 16
 
1.6%
Superlative 16
 
1.6%
Other values (16) 88
 
8.8%

Length

2025-07-07T08:49:00.071423image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
declarative 646
31.5%
simple 645
31.4%
mystery/revelation 80
 
3.9%
direct 68
 
3.3%
to 64
 
3.1%
according 64
 
3.1%
reveals 63
 
3.1%
attribution 51
 
2.5%
list/numbered 47
 
2.3%
superlative 45
 
2.2%
Other values (24) 281
13.7%

Most occurring characters

ValueCountFrequency (%)
e 2765
14.2%
i 1813
 
9.3%
a 1603
 
8.2%
l 1483
 
7.6%
t 1476
 
7.6%
r 1207
 
6.2%
1072
 
5.5%
c 885
 
4.5%
v 851
 
4.4%
D 715
 
3.7%
Other values (32) 5589
28.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19459
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2765
14.2%
i 1813
 
9.3%
a 1603
 
8.2%
l 1483
 
7.6%
t 1476
 
7.6%
r 1207
 
6.2%
1072
 
5.5%
c 885
 
4.5%
v 851
 
4.4%
D 715
 
3.7%
Other values (32) 5589
28.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19459
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2765
14.2%
i 1813
 
9.3%
a 1603
 
8.2%
l 1483
 
7.6%
t 1476
 
7.6%
r 1207
 
6.2%
1072
 
5.5%
c 885
 
4.5%
v 851
 
4.4%
D 715
 
3.7%
Other values (32) 5589
28.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19459
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2765
14.2%
i 1813
 
9.3%
a 1603
 
8.2%
l 1483
 
7.6%
t 1476
 
7.6%
r 1207
 
6.2%
1072
 
5.5%
c 885
 
4.5%
v 851
 
4.4%
D 715
 
3.7%
Other values (32) 5589
28.7%
Distinct978
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Memory size178.8 KiB
2025-07-07T08:49:00.384191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length295
Median length173
Mean length125.14515
Min length0

Characters and Unicode

Total characters125020
Distinct characters76
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique972 ?
Unique (%)97.3%

Sample

1st rowIt is a straightforward statement of fact, reporting an event without any special framing.
2nd rowThe headline makes a direct statement about a new event or situation without asking a question or using sensational language.
3rd rowThe headline uses the superlative "best" to describe the day trip experience.
4th rowThe headline states a fact directly without questions, quotes, or other complex structures.
5th rowThe headline compares a new "smarter option" to traditional "parasols", emphasizing its superior performance ("covers... better").
ValueCountFrequency (%)
a 1848
 
9.8%
the 1474
 
7.8%
headline 928
 
4.9%
or 642
 
3.4%
without 523
 
2.8%
statement 457
 
2.4%
about 400
 
2.1%
question 381
 
2.0%
direct 373
 
2.0%
and 363
 
1.9%
Other values (1609) 11546
61.0%
2025-07-07T08:49:00.909466image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17953
14.4%
e 12895
 
10.3%
t 10865
 
8.7%
a 9153
 
7.3%
i 8878
 
7.1%
n 8041
 
6.4%
s 6632
 
5.3%
o 6307
 
5.0%
r 5641
 
4.5%
h 3983
 
3.2%
Other values (66) 34672
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 125020
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
17953
14.4%
e 12895
 
10.3%
t 10865
 
8.7%
a 9153
 
7.3%
i 8878
 
7.1%
n 8041
 
6.4%
s 6632
 
5.3%
o 6307
 
5.0%
r 5641
 
4.5%
h 3983
 
3.2%
Other values (66) 34672
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 125020
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
17953
14.4%
e 12895
 
10.3%
t 10865
 
8.7%
a 9153
 
7.3%
i 8878
 
7.1%
n 8041
 
6.4%
s 6632
 
5.3%
o 6307
 
5.0%
r 5641
 
4.5%
h 3983
 
3.2%
Other values (66) 34672
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 125020
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
17953
14.4%
e 12895
 
10.3%
t 10865
 
8.7%
a 9153
 
7.3%
i 8878
 
7.1%
n 8041
 
6.4%
s 6632
 
5.3%
o 6307
 
5.0%
r 5641
 
4.5%
h 3983
 
3.2%
Other values (66) 34672
27.7%

Temporal_Urgency_Present
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.8 KiB
No
827 
Yes
155 
 
17

Length

Max length3
Median length2
Mean length2.1211211
Min length0

Characters and Unicode

Total characters2119
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 827
82.8%
Yes 155
 
15.5%
17
 
1.7%

Length

2025-07-07T08:49:01.037093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:49:01.105057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 827
84.2%
yes 155
 
15.8%

Most occurring characters

ValueCountFrequency (%)
N 827
39.0%
o 827
39.0%
Y 155
 
7.3%
e 155
 
7.3%
s 155
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2119
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 827
39.0%
o 827
39.0%
Y 155
 
7.3%
e 155
 
7.3%
s 155
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2119
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 827
39.0%
o 827
39.0%
Y 155
 
7.3%
e 155
 
7.3%
s 155
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2119
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 827
39.0%
o 827
39.0%
Y 155
 
7.3%
e 155
 
7.3%
s 155
 
7.3%
Distinct107
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Memory size59.4 KiB
2025-07-07T08:49:01.283384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length38
Median length2
Mean length3.7407407
Min length0

Characters and Unicode

Total characters3737
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)8.6%

Sample

1st row[]
2nd row["begun"]
3rd row[]
4th row[]
5th row[]
ValueCountFrequency (%)
827
77.6%
week 16
 
1.5%
now 14
 
1.3%
next 13
 
1.2%
this 11
 
1.0%
june 9
 
0.8%
immediately 6
 
0.6%
hours 6
 
0.6%
today 5
 
0.5%
before 5
 
0.5%
Other values (101) 154
 
14.4%
2025-07-07T08:49:01.639127image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 982
26.3%
] 982
26.3%
" 358
 
9.6%
e 167
 
4.5%
t 114
 
3.1%
n 101
 
2.7%
a 92
 
2.5%
84
 
2.2%
o 82
 
2.2%
i 74
 
2.0%
Other values (45) 701
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3737
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 982
26.3%
] 982
26.3%
" 358
 
9.6%
e 167
 
4.5%
t 114
 
3.1%
n 101
 
2.7%
a 92
 
2.5%
84
 
2.2%
o 82
 
2.2%
i 74
 
2.0%
Other values (45) 701
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3737
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 982
26.3%
] 982
26.3%
" 358
 
9.6%
e 167
 
4.5%
t 114
 
3.1%
n 101
 
2.7%
a 92
 
2.5%
84
 
2.2%
o 82
 
2.2%
i 74
 
2.0%
Other values (45) 701
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3737
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 982
26.3%
] 982
26.3%
" 358
 
9.6%
e 167
 
4.5%
t 114
 
3.1%
n 101
 
2.7%
a 92
 
2.5%
84
 
2.2%
o 82
 
2.2%
i 74
 
2.0%
Other values (45) 701
18.8%

Exclusivity_Present
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
No
942 
Yes
 
40
 
17

Length

Max length3
Median length2
Mean length2.006006
Min length0

Characters and Unicode

Total characters2004
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 942
94.3%
Yes 40
 
4.0%
17
 
1.7%

Length

2025-07-07T08:49:01.770683image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:49:01.842403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 942
95.9%
yes 40
 
4.1%

Most occurring characters

ValueCountFrequency (%)
N 942
47.0%
o 942
47.0%
Y 40
 
2.0%
e 40
 
2.0%
s 40
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 942
47.0%
o 942
47.0%
Y 40
 
2.0%
e 40
 
2.0%
s 40
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 942
47.0%
o 942
47.0%
Y 40
 
2.0%
e 40
 
2.0%
s 40
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 942
47.0%
o 942
47.0%
Y 40
 
2.0%
e 40
 
2.0%
s 40
 
2.0%

Exclusivity_Words
Categorical

High correlation  Imbalance 

Distinct33
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size58.1 KiB
[]
942 
 
17
["only"]
 
6
["unique"]
 
2
["smallest"]
 
2
Other values (28)
 
30

Length

Max length39
Median length2
Mean length2.3723724
Min length0

Characters and Unicode

Total characters2370
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)2.6%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[]
5th row[]

Common Values

ValueCountFrequency (%)
[] 942
94.3%
17
 
1.7%
["only"] 6
 
0.6%
["unique"] 2
 
0.2%
["smallest"] 2
 
0.2%
["lesser-known"] 2
 
0.2%
["single"] 2
 
0.2%
["1 thing"] 1
 
0.1%
["last"] 1
 
0.1%
["sole"] 1
 
0.1%
Other values (23) 23
 
2.3%

Length

2025-07-07T08:49:01.940996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
942
94.5%
only 6
 
0.6%
1 3
 
0.3%
first 3
 
0.3%
smallest 2
 
0.2%
lesser-known 2
 
0.2%
single 2
 
0.2%
unique 2
 
0.2%
one 2
 
0.2%
time 2
 
0.2%
Other values (30) 31
 
3.1%

Most occurring characters

ValueCountFrequency (%)
[ 982
41.4%
] 982
41.4%
" 84
 
3.5%
e 46
 
1.9%
s 29
 
1.2%
t 26
 
1.1%
n 24
 
1.0%
r 21
 
0.9%
i 21
 
0.9%
l 20
 
0.8%
Other values (24) 135
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2370
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 982
41.4%
] 982
41.4%
" 84
 
3.5%
e 46
 
1.9%
s 29
 
1.2%
t 26
 
1.1%
n 24
 
1.0%
r 21
 
0.9%
i 21
 
0.9%
l 20
 
0.8%
Other values (24) 135
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2370
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 982
41.4%
] 982
41.4%
" 84
 
3.5%
e 46
 
1.9%
s 29
 
1.2%
t 26
 
1.1%
n 24
 
1.0%
r 21
 
0.9%
i 21
 
0.9%
l 20
 
0.8%
Other values (24) 135
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2370
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 982
41.4%
] 982
41.4%
" 84
 
3.5%
e 46
 
1.9%
s 29
 
1.2%
t 26
 
1.1%
n 24
 
1.0%
r 21
 
0.9%
i 21
 
0.9%
l 20
 
0.8%
Other values (24) 135
 
5.7%

Authority_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
No
772 
Yes
210 
 
17

Length

Max length3
Median length2
Mean length2.1761762
Min length0

Characters and Unicode

Total characters2174
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 772
77.3%
Yes 210
 
21.0%
17
 
1.7%

Length

2025-07-07T08:49:02.062399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:49:02.136563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 772
78.6%
yes 210
 
21.4%

Most occurring characters

ValueCountFrequency (%)
N 772
35.5%
o 772
35.5%
Y 210
 
9.7%
e 210
 
9.7%
s 210
 
9.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2174
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 772
35.5%
o 772
35.5%
Y 210
 
9.7%
e 210
 
9.7%
s 210
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2174
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 772
35.5%
o 772
35.5%
Y 210
 
9.7%
e 210
 
9.7%
s 210
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2174
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 772
35.5%
o 772
35.5%
Y 210
 
9.7%
e 210
 
9.7%
s 210
 
9.7%
Distinct140
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size60.3 KiB
2025-07-07T08:49:02.360031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length61
Median length2
Mean length4.6646647
Min length0

Characters and Unicode

Total characters4660
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique109 ?
Unique (%)10.9%

Sample

1st row[]
2nd row[]
3rd row[]
4th row["convicted"]
5th row[]
ValueCountFrequency (%)
772
73.9%
confirms 15
 
1.4%
dwp 12
 
1.1%
hmrc 9
 
0.9%
experts 7
 
0.7%
told 6
 
0.6%
met 6
 
0.6%
office 5
 
0.5%
urged 5
 
0.5%
expert 4
 
0.4%
Other values (163) 203
 
19.4%
2025-07-07T08:49:02.785800image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 982
21.1%
] 982
21.1%
" 546
11.7%
e 220
 
4.7%
s 162
 
3.5%
r 157
 
3.4%
n 137
 
2.9%
i 136
 
2.9%
o 118
 
2.5%
a 118
 
2.5%
Other values (46) 1102
23.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4660
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 982
21.1%
] 982
21.1%
" 546
11.7%
e 220
 
4.7%
s 162
 
3.5%
r 157
 
3.4%
n 137
 
2.9%
i 136
 
2.9%
o 118
 
2.5%
a 118
 
2.5%
Other values (46) 1102
23.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4660
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 982
21.1%
] 982
21.1%
" 546
11.7%
e 220
 
4.7%
s 162
 
3.5%
r 157
 
3.4%
n 137
 
2.9%
i 136
 
2.9%
o 118
 
2.5%
a 118
 
2.5%
Other values (46) 1102
23.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4660
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 982
21.1%
] 982
21.1%
" 546
11.7%
e 220
 
4.7%
s 162
 
3.5%
r 157
 
3.4%
n 137
 
2.9%
i 136
 
2.9%
o 118
 
2.5%
a 118
 
2.5%
Other values (46) 1102
23.6%

Solution_Present
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.8 KiB
No
845 
Yes
137 
 
17

Length

Max length3
Median length2
Mean length2.1031031
Min length0

Characters and Unicode

Total characters2101
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowNo
5th rowYes

Common Values

ValueCountFrequency (%)
No 845
84.6%
Yes 137
 
13.7%
17
 
1.7%

Length

2025-07-07T08:49:02.910946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:49:02.983841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 845
86.0%
yes 137
 
14.0%

Most occurring characters

ValueCountFrequency (%)
N 845
40.2%
o 845
40.2%
Y 137
 
6.5%
e 137
 
6.5%
s 137
 
6.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2101
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 845
40.2%
o 845
40.2%
Y 137
 
6.5%
e 137
 
6.5%
s 137
 
6.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2101
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 845
40.2%
o 845
40.2%
Y 137
 
6.5%
e 137
 
6.5%
s 137
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2101
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 845
40.2%
o 845
40.2%
Y 137
 
6.5%
e 137
 
6.5%
s 137
 
6.5%
Distinct128
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Memory size60.2 KiB
2025-07-07T08:49:03.171987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length67
Median length2
Mean length4.3373373
Min length0

Characters and Unicode

Total characters4333
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique117 ?
Unique (%)11.7%

Sample

1st row[]
2nd row[]
3rd row["best"]
4th row[]
5th row["smarter option","better"]
ValueCountFrequency (%)
845
76.0%
best 4
 
0.4%
ingredient 4
 
0.4%
item 4
 
0.4%
better 4
 
0.4%
put 3
 
0.3%
stay 3
 
0.3%
method 3
 
0.3%
to 3
 
0.3%
free 3
 
0.3%
Other values (210) 236
 
21.2%
2025-07-07T08:49:03.715397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 982
22.7%
] 982
22.7%
" 386
 
8.9%
e 236
 
5.4%
t 184
 
4.2%
r 134
 
3.1%
130
 
3.0%
i 122
 
2.8%
n 117
 
2.7%
s 115
 
2.7%
Other values (39) 945
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4333
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 982
22.7%
] 982
22.7%
" 386
 
8.9%
e 236
 
5.4%
t 184
 
4.2%
r 134
 
3.1%
130
 
3.0%
i 122
 
2.8%
n 117
 
2.7%
s 115
 
2.7%
Other values (39) 945
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4333
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 982
22.7%
] 982
22.7%
" 386
 
8.9%
e 236
 
5.4%
t 184
 
4.2%
r 134
 
3.1%
130
 
3.0%
i 122
 
2.8%
n 117
 
2.7%
s 115
 
2.7%
Other values (39) 945
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4333
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 982
22.7%
] 982
22.7%
" 386
 
8.9%
e 236
 
5.4%
t 184
 
4.2%
r 134
 
3.1%
130
 
3.0%
i 122
 
2.8%
n 117
 
2.7%
s 115
 
2.7%
Other values (39) 945
21.8%

Economic_Benefit_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.8 KiB
No
791 
Yes
191 
 
17

Length

Max length3
Median length2
Mean length2.1571572
Min length0

Characters and Unicode

Total characters2155
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 791
79.2%
Yes 191
 
19.1%
17
 
1.7%

Length

2025-07-07T08:49:03.898067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:49:03.998929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 791
80.5%
yes 191
 
19.5%

Most occurring characters

ValueCountFrequency (%)
N 791
36.7%
o 791
36.7%
Y 191
 
8.9%
e 191
 
8.9%
s 191
 
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2155
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 791
36.7%
o 791
36.7%
Y 191
 
8.9%
e 191
 
8.9%
s 191
 
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2155
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 791
36.7%
o 791
36.7%
Y 191
 
8.9%
e 191
 
8.9%
s 191
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2155
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 791
36.7%
o 791
36.7%
Y 191
 
8.9%
e 191
 
8.9%
s 191
 
8.9%
Distinct159
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size64.4 KiB
2025-07-07T08:49:04.258071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length56
Median length2
Mean length4.994995
Min length0

Characters and Unicode

Total characters4990
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique139 ?
Unique (%)13.9%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[]
5th row[]
ValueCountFrequency (%)
791
74.1%
free 13
 
1.2%
payment 8
 
0.7%
payments 8
 
0.7%
of 7
 
0.7%
living 6
 
0.6%
fuel 5
 
0.5%
£300","payment 4
 
0.4%
cash 4
 
0.4%
cheap 4
 
0.4%
Other values (180) 218
 
20.4%
2025-07-07T08:49:04.762522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 982
19.7%
] 982
19.7%
" 630
12.6%
e 231
 
4.6%
n 170
 
3.4%
a 151
 
3.0%
, 142
 
2.8%
s 131
 
2.6%
t 128
 
2.6%
i 120
 
2.4%
Other values (52) 1323
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4990
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 982
19.7%
] 982
19.7%
" 630
12.6%
e 231
 
4.6%
n 170
 
3.4%
a 151
 
3.0%
, 142
 
2.8%
s 131
 
2.6%
t 128
 
2.6%
i 120
 
2.4%
Other values (52) 1323
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4990
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 982
19.7%
] 982
19.7%
" 630
12.6%
e 231
 
4.6%
n 170
 
3.4%
a 151
 
3.0%
, 142
 
2.8%
s 131
 
2.6%
t 128
 
2.6%
i 120
 
2.4%
Other values (52) 1323
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4990
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 982
19.7%
] 982
19.7%
" 630
12.6%
e 231
 
4.6%
n 170
 
3.4%
a 151
 
3.0%
, 142
 
2.8%
s 131
 
2.6%
t 128
 
2.6%
i 120
 
2.4%
Other values (52) 1323
26.5%

Prohibition_Restriction_Present
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
No
891 
Yes
91 
 
17

Length

Max length3
Median length2
Mean length2.0570571
Min length0

Characters and Unicode

Total characters2055
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 891
89.2%
Yes 91
 
9.1%
17
 
1.7%

Length

2025-07-07T08:49:04.936307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:49:05.048531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 891
90.7%
yes 91
 
9.3%

Most occurring characters

ValueCountFrequency (%)
N 891
43.4%
o 891
43.4%
Y 91
 
4.4%
e 91
 
4.4%
s 91
 
4.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2055
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 891
43.4%
o 891
43.4%
Y 91
 
4.4%
e 91
 
4.4%
s 91
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2055
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 891
43.4%
o 891
43.4%
Y 91
 
4.4%
e 91
 
4.4%
s 91
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2055
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 891
43.4%
o 891
43.4%
Y 91
 
4.4%
e 91
 
4.4%
s 91
 
4.4%
Distinct69
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size58.9 KiB
2025-07-07T08:49:05.297608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length47
Median length2
Mean length3.0950951
Min length0

Characters and Unicode

Total characters3092
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)5.5%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[]
5th row[]
ValueCountFrequency (%)
891
84.7%
not 9
 
0.9%
stop 7
 
0.7%
banned 6
 
0.6%
ban 6
 
0.6%
to 6
 
0.6%
rule 5
 
0.5%
no 3
 
0.3%
cancel 3
 
0.3%
fine 3
 
0.3%
Other values (93) 113
 
10.7%
2025-07-07T08:49:05.785760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
[ 982
31.8%
] 982
31.8%
" 210
 
6.8%
e 95
 
3.1%
n 87
 
2.8%
70
 
2.3%
o 69
 
2.2%
i 60
 
1.9%
t 58
 
1.9%
a 55
 
1.8%
Other values (32) 424
13.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
[ 982
31.8%
] 982
31.8%
" 210
 
6.8%
e 95
 
3.1%
n 87
 
2.8%
70
 
2.3%
o 69
 
2.2%
i 60
 
1.9%
t 58
 
1.9%
a 55
 
1.8%
Other values (32) 424
13.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
[ 982
31.8%
] 982
31.8%
" 210
 
6.8%
e 95
 
3.1%
n 87
 
2.8%
70
 
2.3%
o 69
 
2.2%
i 60
 
1.9%
t 58
 
1.9%
a 55
 
1.8%
Other values (32) 424
13.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
[ 982
31.8%
] 982
31.8%
" 210
 
6.8%
e 95
 
3.1%
n 87
 
2.8%
70
 
2.3%
o 69
 
2.2%
i 60
 
1.9%
t 58
 
1.9%
a 55
 
1.8%
Other values (32) 424
13.7%

National_Relevance_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.1 KiB
No
546 
Yes
436 
 
17

Length

Max length3
Median length2
Mean length2.4024024
Min length0

Characters and Unicode

Total characters2400
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowYes
3rd rowYes
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 546
54.7%
Yes 436
43.6%
17
 
1.7%

Length

2025-07-07T08:49:05.983889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:49:06.068022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 546
55.6%
yes 436
44.4%

Most occurring characters

ValueCountFrequency (%)
N 546
22.8%
o 546
22.8%
Y 436
18.2%
e 436
18.2%
s 436
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2400
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 546
22.8%
o 546
22.8%
Y 436
18.2%
e 436
18.2%
s 436
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2400
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 546
22.8%
o 546
22.8%
Y 436
18.2%
e 436
18.2%
s 436
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2400
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 546
22.8%
o 546
22.8%
Y 436
18.2%
e 436
18.2%
s 436
18.2%
Distinct211
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Memory size62.9 KiB
2025-07-07T08:49:06.214097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length91
Median length2
Mean length7.1031031
Min length0

Characters and Unicode

Total characters7096
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique167 ?
Unique (%)16.7%

Sample

1st row["British","Russian"]
2nd row["England"]
3rd row["English","London"]
4th row[]
5th row[]
ValueCountFrequency (%)
546
51.9%
uk 101
 
9.6%
england 45
 
4.3%
london 16
 
1.5%
dwp 9
 
0.9%
manchester 8
 
0.8%
british 7
 
0.7%
hmrc 7
 
0.7%
britain 6
 
0.6%
households 5
 
0.5%
Other values (230) 303
28.8%
2025-07-07T08:49:06.542830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 1204
17.0%
[ 982
13.8%
] 982
13.8%
n 410
 
5.8%
a 279
 
3.9%
r 223
 
3.1%
e 214
 
3.0%
i 212
 
3.0%
s 203
 
2.9%
o 203
 
2.9%
Other values (50) 2184
30.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
" 1204
17.0%
[ 982
13.8%
] 982
13.8%
n 410
 
5.8%
a 279
 
3.9%
r 223
 
3.1%
e 214
 
3.0%
i 212
 
3.0%
s 203
 
2.9%
o 203
 
2.9%
Other values (50) 2184
30.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
" 1204
17.0%
[ 982
13.8%
] 982
13.8%
n 410
 
5.8%
a 279
 
3.9%
r 223
 
3.1%
e 214
 
3.0%
i 212
 
3.0%
s 203
 
2.9%
o 203
 
2.9%
Other values (50) 2184
30.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
" 1204
17.0%
[ 982
13.8%
] 982
13.8%
n 410
 
5.8%
a 279
 
3.9%
r 223
 
3.1%
e 214
 
3.0%
i 212
 
3.0%
s 203
 
2.9%
o 203
 
2.9%
Other values (50) 2184
30.8%

Recognized_Brand_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
No
641 
Yes
341 
 
17

Length

Max length3
Median length2
Mean length2.3073073
Min length0

Characters and Unicode

Total characters2305
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowYes

Common Values

ValueCountFrequency (%)
No 641
64.2%
Yes 341
34.1%
17
 
1.7%

Length

2025-07-07T08:49:06.674287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:49:06.749273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 641
65.3%
yes 341
34.7%

Most occurring characters

ValueCountFrequency (%)
N 641
27.8%
o 641
27.8%
Y 341
14.8%
e 341
14.8%
s 341
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2305
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 641
27.8%
o 641
27.8%
Y 341
14.8%
e 341
14.8%
s 341
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2305
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 641
27.8%
o 641
27.8%
Y 341
14.8%
e 341
14.8%
s 341
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2305
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 641
27.8%
o 641
27.8%
Y 341
14.8%
e 341
14.8%
s 341
14.8%
Distinct232
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Memory size63.2 KiB
2025-07-07T08:49:06.947394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length65
Median length2
Mean length7.3883884
Min length0

Characters and Unicode

Total characters7381
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique194 ?
Unique (%)19.4%

Sample

1st row[]
2nd row[]
3rd row[]
4th row[]
5th row["Tesco"]
ValueCountFrequency (%)
641
53.6%
dwp 26
 
2.2%
air 12
 
1.0%
hmrc 11
 
0.9%
india 9
 
0.8%
glastonbury 7
 
0.6%
the 6
 
0.5%
bbc 6
 
0.5%
british 6
 
0.5%
aldi 6
 
0.5%
Other values (348) 465
38.9%
2025-07-07T08:49:07.341229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
" 1004
 
13.6%
[ 982
 
13.3%
] 982
 
13.3%
a 361
 
4.9%
e 307
 
4.2%
r 276
 
3.7%
n 255
 
3.5%
i 253
 
3.4%
s 214
 
2.9%
213
 
2.9%
Other values (59) 2534
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7381
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
" 1004
 
13.6%
[ 982
 
13.3%
] 982
 
13.3%
a 361
 
4.9%
e 307
 
4.2%
r 276
 
3.7%
n 255
 
3.5%
i 253
 
3.4%
s 214
 
2.9%
213
 
2.9%
Other values (59) 2534
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7381
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
" 1004
 
13.6%
[ 982
 
13.3%
] 982
 
13.3%
a 361
 
4.9%
e 307
 
4.2%
r 276
 
3.7%
n 255
 
3.5%
i 253
 
3.4%
s 214
 
2.9%
213
 
2.9%
Other values (59) 2534
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7381
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
" 1004
 
13.6%
[ 982
 
13.3%
] 982
 
13.3%
a 361
 
4.9%
e 307
 
4.2%
r 276
 
3.7%
n 255
 
3.5%
i 253
 
3.4%
s 214
 
2.9%
213
 
2.9%
Other values (59) 2534
34.3%

Curiosity_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.4 KiB
Yes
750 
No
232 
 
17

Length

Max length3
Median length3
Mean length2.7167167
Min length0

Characters and Unicode

Total characters2714
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowYes
4th rowYes
5th rowYes

Common Values

ValueCountFrequency (%)
Yes 750
75.1%
No 232
 
23.2%
17
 
1.7%

Length

2025-07-07T08:49:07.469381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:49:07.540938image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yes 750
76.4%
no 232
 
23.6%

Most occurring characters

ValueCountFrequency (%)
Y 750
27.6%
e 750
27.6%
s 750
27.6%
N 232
 
8.5%
o 232
 
8.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2714
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 750
27.6%
e 750
27.6%
s 750
27.6%
N 232
 
8.5%
o 232
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2714
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 750
27.6%
e 750
27.6%
s 750
27.6%
N 232
 
8.5%
o 232
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2714
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 750
27.6%
e 750
27.6%
s 750
27.6%
N 232
 
8.5%
o 232
 
8.5%
Distinct754
Distinct (%)75.5%
Missing0
Missing (%)0.0%
Memory size163.0 KiB
2025-07-07T08:49:07.789911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length249
Median length194
Mean length100.38138
Min length0

Characters and Unicode

Total characters100281
Distinct characters87
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique753 ?
Unique (%)75.4%

Sample

1st row
2nd rowThe headline creates an information gap by mentioning a 'new driveway rule change' without specifying what the change is, prompting the reader to click to find out.
3rd rowThe headline creates curiosity by promising the "best day trip" from London without immediately revealing the city.
4th rowThe unusual act of 'covering road with potatoes and silt' creates a strong information gap and makes the reader want to know the backstory.
5th rowThe phrase "smarter option that covers your entire outdoor space better" creates an information gap, prompting the reader to discover what this new solution is.
ValueCountFrequency (%)
the 1745
 
10.8%
and 538
 
3.3%
to 536
 
3.3%
creates 496
 
3.1%
information 492
 
3.0%
gap 479
 
3.0%
an 468
 
2.9%
about 386
 
2.4%
phrase 331
 
2.0%
of 298
 
1.8%
Other values (2201) 10378
64.3%
2025-07-07T08:49:08.253722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15396
15.4%
e 9733
 
9.7%
a 7503
 
7.5%
t 7327
 
7.3%
i 6420
 
6.4%
n 6050
 
6.0%
r 5685
 
5.7%
o 5563
 
5.5%
s 4914
 
4.9%
h 3972
 
4.0%
Other values (77) 27718
27.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 100281
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15396
15.4%
e 9733
 
9.7%
a 7503
 
7.5%
t 7327
 
7.3%
i 6420
 
6.4%
n 6050
 
6.0%
r 5685
 
5.7%
o 5563
 
5.5%
s 4914
 
4.9%
h 3972
 
4.0%
Other values (77) 27718
27.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 100281
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15396
15.4%
e 9733
 
9.7%
a 7503
 
7.5%
t 7327
 
7.3%
i 6420
 
6.4%
n 6050
 
6.0%
r 5685
 
5.7%
o 5563
 
5.5%
s 4914
 
4.9%
h 3972
 
4.0%
Other values (77) 27718
27.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 100281
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15396
15.4%
e 9733
 
9.7%
a 7503
 
7.5%
t 7327
 
7.3%
i 6420
 
6.4%
n 6050
 
6.0%
r 5685
 
5.7%
o 5563
 
5.5%
s 4914
 
4.9%
h 3972
 
4.0%
Other values (77) 27718
27.6%

Fear_Concern_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.1 KiB
No
519 
Yes
463 
 
17

Length

Max length3
Median length2
Mean length2.4294294
Min length0

Characters and Unicode

Total characters2427
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowYes
2nd rowYes
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 519
52.0%
Yes 463
46.3%
17
 
1.7%

Length

2025-07-07T08:49:08.377592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:49:08.453219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 519
52.9%
yes 463
47.1%

Most occurring characters

ValueCountFrequency (%)
N 519
21.4%
o 519
21.4%
Y 463
19.1%
e 463
19.1%
s 463
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2427
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 519
21.4%
o 519
21.4%
Y 463
19.1%
e 463
19.1%
s 463
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2427
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 519
21.4%
o 519
21.4%
Y 463
19.1%
e 463
19.1%
s 463
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2427
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 519
21.4%
o 519
21.4%
Y 463
19.1%
e 463
19.1%
s 463
19.1%
Distinct469
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Memory size116.5 KiB
2025-07-07T08:49:08.745212image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length227
Median length0
Mean length56.201201
Min length0

Characters and Unicode

Total characters56145
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique468 ?
Unique (%)46.8%

Sample

1st rowThe mention of 'intercept' and 'military aircraft' implies a potential threat or escalating tensions.
2nd rowThe phrase 'affecting every home' can generate concern or fear among homeowners about potential negative consequences or obligations.
3rd row
4th row
5th row
ValueCountFrequency (%)
the 575
 
6.8%
concern 384
 
4.5%
and 380
 
4.5%
of 297
 
3.5%
a 289
 
3.4%
or 206
 
2.4%
potential 173
 
2.0%
for 172
 
2.0%
about 167
 
2.0%
to 139
 
1.6%
Other values (1621) 5683
67.1%
2025-07-07T08:49:09.836427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7997
14.2%
e 5962
 
10.6%
n 4153
 
7.4%
a 3661
 
6.5%
o 3634
 
6.5%
t 3459
 
6.2%
i 3275
 
5.8%
r 3177
 
5.7%
s 2721
 
4.8%
c 2383
 
4.2%
Other values (68) 15723
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 56145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7997
14.2%
e 5962
 
10.6%
n 4153
 
7.4%
a 3661
 
6.5%
o 3634
 
6.5%
t 3459
 
6.2%
i 3275
 
5.8%
r 3177
 
5.7%
s 2721
 
4.8%
c 2383
 
4.2%
Other values (68) 15723
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 56145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7997
14.2%
e 5962
 
10.6%
n 4153
 
7.4%
a 3661
 
6.5%
o 3634
 
6.5%
t 3459
 
6.2%
i 3275
 
5.8%
r 3177
 
5.7%
s 2721
 
4.8%
c 2383
 
4.2%
Other values (68) 15723
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 56145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7997
14.2%
e 5962
 
10.6%
n 4153
 
7.4%
a 3661
 
6.5%
o 3634
 
6.5%
t 3459
 
6.2%
i 3275
 
5.8%
r 3177
 
5.7%
s 2721
 
4.8%
c 2383
 
4.2%
Other values (68) 15723
28.0%

Surprise_Awe_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
No
732 
Yes
250 
 
17

Length

Max length3
Median length2
Mean length2.2162162
Min length0

Characters and Unicode

Total characters2214
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowYes
5th rowNo

Common Values

ValueCountFrequency (%)
No 732
73.3%
Yes 250
 
25.0%
17
 
1.7%

Length

2025-07-07T08:49:09.970266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:49:10.062765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 732
74.5%
yes 250
 
25.5%

Most occurring characters

ValueCountFrequency (%)
N 732
33.1%
o 732
33.1%
Y 250
 
11.3%
e 250
 
11.3%
s 250
 
11.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2214
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 732
33.1%
o 732
33.1%
Y 250
 
11.3%
e 250
 
11.3%
s 250
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2214
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 732
33.1%
o 732
33.1%
Y 250
 
11.3%
e 250
 
11.3%
s 250
 
11.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2214
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 732
33.1%
o 732
33.1%
Y 250
 
11.3%
e 250
 
11.3%
s 250
 
11.3%
Distinct255
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Memory size87.1 KiB
2025-07-07T08:49:10.364479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length184
Median length0
Mean length27.762763
Min length0

Characters and Unicode

Total characters27735
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique254 ?
Unique (%)25.4%

Sample

1st row
2nd row
3rd row
4th rowThe combination of 'potatoes and silt' as a method for a crime is unexpected and bizarre.
5th row
ValueCountFrequency (%)
the 363
 
8.1%
of 262
 
5.8%
a 257
 
5.7%
and 197
 
4.4%
surprise 133
 
3.0%
unexpected 100
 
2.2%
is 94
 
2.1%
can 83
 
1.8%
surprising 73
 
1.6%
evoke 69
 
1.5%
Other values (1219) 2865
63.7%
2025-07-07T08:49:10.890039image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4243
15.3%
e 3037
 
11.0%
n 1830
 
6.6%
i 1757
 
6.3%
a 1734
 
6.3%
s 1677
 
6.0%
t 1519
 
5.5%
r 1442
 
5.2%
o 1385
 
5.0%
c 825
 
3.0%
Other values (70) 8286
29.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 27735
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4243
15.3%
e 3037
 
11.0%
n 1830
 
6.6%
i 1757
 
6.3%
a 1734
 
6.3%
s 1677
 
6.0%
t 1519
 
5.5%
r 1442
 
5.2%
o 1385
 
5.0%
c 825
 
3.0%
Other values (70) 8286
29.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 27735
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4243
15.3%
e 3037
 
11.0%
n 1830
 
6.6%
i 1757
 
6.3%
a 1734
 
6.3%
s 1677
 
6.0%
t 1519
 
5.5%
r 1442
 
5.2%
o 1385
 
5.0%
c 825
 
3.0%
Other values (70) 8286
29.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 27735
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4243
15.3%
e 3037
 
11.0%
n 1830
 
6.6%
i 1757
 
6.3%
a 1734
 
6.3%
s 1677
 
6.0%
t 1519
 
5.5%
r 1442
 
5.2%
o 1385
 
5.0%
c 825
 
3.0%
Other values (70) 8286
29.9%

Indignation_Controversy_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size57.8 KiB
No
801 
Yes
181 
 
17

Length

Max length3
Median length2
Mean length2.1471471
Min length0

Characters and Unicode

Total characters2145
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowNo
4th rowNo
5th rowNo

Common Values

ValueCountFrequency (%)
No 801
80.2%
Yes 181
 
18.1%
17
 
1.7%

Length

2025-07-07T08:49:11.026827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:49:11.106233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 801
81.6%
yes 181
 
18.4%

Most occurring characters

ValueCountFrequency (%)
N 801
37.3%
o 801
37.3%
Y 181
 
8.4%
e 181
 
8.4%
s 181
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 801
37.3%
o 801
37.3%
Y 181
 
8.4%
e 181
 
8.4%
s 181
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 801
37.3%
o 801
37.3%
Y 181
 
8.4%
e 181
 
8.4%
s 181
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 801
37.3%
o 801
37.3%
Y 181
 
8.4%
e 181
 
8.4%
s 181
 
8.4%
Distinct188
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Memory size80.6 KiB
2025-07-07T08:49:11.401580image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length242
Median length0
Mean length23.992993
Min length0

Characters and Unicode

Total characters23969
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)18.7%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
the 250
 
7.0%
and 161
 
4.5%
of 128
 
3.6%
indignation 119
 
3.3%
a 117
 
3.3%
or 115
 
3.2%
debate 101
 
2.8%
to 93
 
2.6%
provoke 71
 
2.0%
can 68
 
1.9%
Other values (974) 2361
65.9%
2025-07-07T08:49:11.897607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3397
14.2%
e 2205
 
9.2%
n 1859
 
7.8%
i 1771
 
7.4%
o 1725
 
7.2%
a 1698
 
7.1%
t 1604
 
6.7%
r 1228
 
5.1%
s 1073
 
4.5%
d 806
 
3.4%
Other values (59) 6603
27.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23969
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3397
14.2%
e 2205
 
9.2%
n 1859
 
7.8%
i 1771
 
7.4%
o 1725
 
7.2%
a 1698
 
7.1%
t 1604
 
6.7%
r 1228
 
5.1%
s 1073
 
4.5%
d 806
 
3.4%
Other values (59) 6603
27.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23969
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3397
14.2%
e 2205
 
9.2%
n 1859
 
7.8%
i 1771
 
7.4%
o 1725
 
7.2%
a 1698
 
7.1%
t 1604
 
6.7%
r 1228
 
5.1%
s 1073
 
4.5%
d 806
 
3.4%
Other values (59) 6603
27.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23969
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3397
14.2%
e 2205
 
9.2%
n 1859
 
7.8%
i 1771
 
7.4%
o 1725
 
7.2%
a 1698
 
7.1%
t 1604
 
6.7%
r 1228
 
5.1%
s 1073
 
4.5%
d 806
 
3.4%
Other values (59) 6603
27.5%

Hope_Optimism_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.0 KiB
No
659 
Yes
323 
 
17

Length

Max length3
Median length2
Mean length2.2892893
Min length0

Characters and Unicode

Total characters2287
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowNo
3rd rowYes
4th rowNo
5th rowYes

Common Values

ValueCountFrequency (%)
No 659
66.0%
Yes 323
32.3%
17
 
1.7%

Length

2025-07-07T08:49:12.029077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:49:12.115579image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
no 659
67.1%
yes 323
32.9%

Most occurring characters

ValueCountFrequency (%)
N 659
28.8%
o 659
28.8%
Y 323
14.1%
e 323
14.1%
s 323
14.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2287
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 659
28.8%
o 659
28.8%
Y 323
14.1%
e 323
14.1%
s 323
14.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2287
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 659
28.8%
o 659
28.8%
Y 323
14.1%
e 323
14.1%
s 323
14.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2287
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 659
28.8%
o 659
28.8%
Y 323
14.1%
e 323
14.1%
s 323
14.1%
Distinct328
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Memory size94.6 KiB
2025-07-07T08:49:12.366929image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length217
Median length0
Mean length36.078078
Min length0

Characters and Unicode

Total characters36042
Distinct characters77
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique327 ?
Unique (%)32.7%

Sample

1st row
2nd row
3rd rowThe phrase "best day trip" evokes a sense of positive experience and an ideal outcome for readers seeking a trip.
4th row
5th rowThe headline offers a "smarter option" that performs "better," suggesting an improved solution to a common outdoor challenge, thereby instilling hope for a more effective product.
ValueCountFrequency (%)
a 455
 
8.1%
the 359
 
6.4%
and 262
 
4.7%
for 228
 
4.1%
of 214
 
3.8%
positive 210
 
3.8%
hope 147
 
2.6%
to 121
 
2.2%
offers 106
 
1.9%
outcome 70
 
1.3%
Other values (1157) 3416
61.1%
2025-07-07T08:49:12.818717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5262
14.6%
e 3963
 
11.0%
o 2735
 
7.6%
i 2426
 
6.7%
t 2125
 
5.9%
s 2110
 
5.9%
a 2086
 
5.8%
n 2051
 
5.7%
r 1735
 
4.8%
p 1140
 
3.2%
Other values (67) 10409
28.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 36042
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
5262
14.6%
e 3963
 
11.0%
o 2735
 
7.6%
i 2426
 
6.7%
t 2125
 
5.9%
s 2110
 
5.9%
a 2086
 
5.8%
n 2051
 
5.7%
r 1735
 
4.8%
p 1140
 
3.2%
Other values (67) 10409
28.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 36042
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
5262
14.6%
e 3963
 
11.0%
o 2735
 
7.6%
i 2426
 
6.7%
t 2125
 
5.9%
s 2110
 
5.9%
a 2086
 
5.8%
n 2051
 
5.7%
r 1735
 
4.8%
p 1140
 
3.2%
Other values (67) 10409
28.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 36042
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
5262
14.6%
e 3963
 
11.0%
o 2735
 
7.6%
i 2426
 
6.7%
t 2125
 
5.9%
s 2110
 
5.9%
a 2086
 
5.8%
n 2051
 
5.7%
r 1735
 
4.8%
p 1140
 
3.2%
Other values (67) 10409
28.9%

Personal_Identification_Present
Categorical

High correlation 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.4 KiB
Yes
780 
No
202 
 
17

Length

Max length3
Median length3
Mean length2.7467467
Min length0

Characters and Unicode

Total characters2744
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo
2nd rowYes
3rd rowYes
4th rowNo
5th rowYes

Common Values

ValueCountFrequency (%)
Yes 780
78.1%
No 202
 
20.2%
17
 
1.7%

Length

2025-07-07T08:49:12.940014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-07T08:49:13.012334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
yes 780
79.4%
no 202
 
20.6%

Most occurring characters

ValueCountFrequency (%)
Y 780
28.4%
e 780
28.4%
s 780
28.4%
N 202
 
7.4%
o 202
 
7.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2744
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
Y 780
28.4%
e 780
28.4%
s 780
28.4%
N 202
 
7.4%
o 202
 
7.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2744
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
Y 780
28.4%
e 780
28.4%
s 780
28.4%
N 202
 
7.4%
o 202
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2744
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
Y 780
28.4%
e 780
28.4%
s 780
28.4%
N 202
 
7.4%
o 202
 
7.4%
Distinct781
Distinct (%)78.2%
Missing0
Missing (%)0.0%
Memory size150.7 KiB
2025-07-07T08:49:13.349181image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length258
Median length178
Mean length95.17017
Min length0

Characters and Unicode

Total characters95075
Distinct characters80
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique779 ?
Unique (%)78.0%

Sample

1st row
2nd rowDirectly appeals to residents of 'England' and specifically to those who are homeowners ('every home').
3rd rowThe headline directly appeals to anyone interested in day trips from London, allowing them to easily identify with the topic.
4th row
5th rowThe phrase "Say goodbye to parasols" and reference to "your entire outdoor space" directly addresses the reader, implying a common problem they might face and offering a relevant solution.
ValueCountFrequency (%)
the 1034
 
7.1%
to 621
 
4.3%
of 480
 
3.3%
and 445
 
3.1%
directly 414
 
2.9%
with 393
 
2.7%
a 372
 
2.6%
or 321
 
2.2%
identify 262
 
1.8%
who 233
 
1.6%
Other values (1801) 9907
68.4%
2025-07-07T08:49:13.860026image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13702
14.4%
e 10277
 
10.8%
i 6663
 
7.0%
t 6404
 
6.7%
a 6136
 
6.5%
n 5746
 
6.0%
o 5303
 
5.6%
s 5079
 
5.3%
r 5058
 
5.3%
l 3908
 
4.1%
Other values (70) 26799
28.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 95075
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
13702
14.4%
e 10277
 
10.8%
i 6663
 
7.0%
t 6404
 
6.7%
a 6136
 
6.5%
n 5746
 
6.0%
o 5303
 
5.6%
s 5079
 
5.3%
r 5058
 
5.3%
l 3908
 
4.1%
Other values (70) 26799
28.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 95075
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
13702
14.4%
e 10277
 
10.8%
i 6663
 
7.0%
t 6404
 
6.7%
a 6136
 
6.5%
n 5746
 
6.0%
o 5303
 
5.6%
s 5079
 
5.3%
r 5058
 
5.3%
l 3908
 
4.1%
Other values (70) 26799
28.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 95075
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
13702
14.4%
e 10277
 
10.8%
i 6663
 
7.0%
t 6404
 
6.7%
a 6136
 
6.5%
n 5746
 
6.0%
o 5303
 
5.6%
s 5079
 
5.3%
r 5058
 
5.3%
l 3908
 
4.1%
Other values (70) 26799
28.2%

Interactions

2025-07-07T08:48:40.596765image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-07T08:49:14.010894image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Authority_PresentClarity_and_Conciseness_ValueContains_ColonContains_Exclamation_MarkContains_HyphenContains_NumbersContains_Question_MarkContains_QuotesCuriosity_PresentEconomic_Benefit_PresentEmphatic_Capitalization_UsageEnds_With_Question_MarkExclusivity_PresentExclusivity_WordsFear_Concern_PresentHope_Optimism_PresentIndignation_Controversy_PresentLength_General_AssessmentMain_CategoryMain_ClassificationNational_Relevance_PresentOriginality_and_Differentiation_ValuePersonal_Identification_PresentProhibition_Restriction_PresentRecognized_Brand_PresentRelevance_and_Timeliness_ValueSolution_PresentStarts_With_NumberStrategic_Keyword_Usage_ValueSurprise_Awe_PresentTemporal_Urgency_PresentVisibility
Authority_Present1.0000.7060.7060.7060.7070.7070.7060.7060.7060.7100.6990.7060.7070.6950.7070.7070.7070.7070.7190.7370.7070.7080.7080.7080.7120.7090.7100.7060.7090.7070.7061.000
Clarity_and_Conciseness_Value0.7061.0000.7080.7060.7060.7070.7060.7110.7070.7060.5630.7060.7060.5500.7080.7060.7160.8380.5850.5670.7060.5840.7070.7060.7060.5790.7070.7060.5770.7060.7071.000
Contains_Colon0.7060.7081.0000.7120.7070.7060.7080.7120.7070.7080.7180.7080.7070.6910.7080.7080.7070.7130.7000.7420.7060.7060.7060.7090.7070.7060.7070.7070.7070.7060.7061.000
Contains_Exclamation_Mark0.7060.7060.7121.0000.7060.7070.7060.7080.7070.7060.6900.7060.7060.6850.7070.7070.7060.7060.7040.7030.7070.7060.7060.7100.7070.7060.7060.7060.7060.7070.7081.000
Contains_Hyphen0.7070.7060.7070.7061.0000.7110.7070.7070.7150.7060.7070.7070.7070.6990.7080.7080.7060.7110.7020.7070.7060.7120.7070.7060.7080.7060.7080.7080.7070.7080.7061.000
Contains_Numbers0.7070.7070.7060.7070.7111.0000.7070.7130.7070.7340.7000.7070.7070.6970.7070.7160.7090.7070.7190.7280.7060.7090.7150.7070.7070.7090.7110.7190.7090.7070.7071.000
Contains_Question_Mark0.7060.7060.7080.7060.7070.7071.0000.7070.7080.7070.7190.9420.7060.6880.7070.7070.7070.7070.7060.9640.7060.7060.7060.7060.7070.7060.7060.7070.7060.7080.7081.000
Contains_Quotes0.7060.7110.7120.7080.7070.7130.7071.0000.7090.7070.6990.7080.7070.6990.7090.7070.7110.7090.7090.7760.7080.7120.7070.7060.7070.7060.7060.7080.7060.7070.7061.000
Curiosity_Present0.7060.7070.7070.7070.7150.7070.7080.7091.0000.7070.6980.7080.7070.6940.7180.7100.7100.7060.7350.7270.7080.7400.7060.7070.7070.7060.7100.7070.7110.7200.7071.000
Economic_Benefit_Present0.7100.7060.7080.7060.7060.7340.7070.7070.7071.0000.7010.7070.7090.7060.7110.7290.7060.7070.7880.7040.7080.7090.7170.7070.7100.7080.7070.7060.7090.7070.7081.000
Emphatic_Capitalization_Usage0.6990.5630.7180.6900.7070.7000.7190.6990.6980.7011.0000.7020.7030.2290.6990.6990.6980.7030.2290.2260.7010.5660.6950.6980.6990.5590.6960.6900.5570.7000.7051.000
Ends_With_Question_Mark0.7060.7060.7080.7060.7070.7070.9420.7080.7080.7070.7021.0000.7070.6890.7070.7060.7070.7070.7030.9560.7060.7060.7060.7070.7060.7060.7070.7070.7060.7070.7071.000
Exclusivity_Present0.7070.7060.7070.7060.7070.7070.7060.7070.7070.7090.7030.7071.0000.9850.7070.7110.7060.7060.7040.7020.7070.7110.7060.7060.7070.7060.7080.7070.7070.7090.7071.000
Exclusivity_Words0.6950.5500.6910.6850.6990.6970.6880.6990.6940.7060.2290.6890.9851.0000.6960.7020.6940.6980.2490.1900.6970.5680.6940.7010.6980.5660.7050.6850.5510.7020.6971.000
Fear_Concern_Present0.7070.7080.7080.7070.7080.7070.7070.7090.7180.7110.6990.7070.7070.6961.0000.7640.7300.7080.7820.7260.7080.7090.7130.7230.7070.7070.7100.7060.7060.7150.7211.000
Hope_Optimism_Present0.7070.7060.7080.7070.7080.7160.7070.7070.7100.7290.6990.7060.7110.7020.7641.0000.7270.7060.7750.7270.7080.7110.7210.7110.7110.7060.7860.7070.7060.7070.7091.000
Indignation_Controversy_Present0.7070.7160.7070.7060.7060.7090.7070.7110.7100.7060.6980.7070.7060.6940.7300.7271.0000.7150.7310.7090.7070.7120.7090.7180.7060.7060.7110.7060.7060.7110.7081.000
Length_General_Assessment0.7070.8380.7130.7060.7110.7070.7070.7090.7060.7070.7030.7070.7060.6980.7080.7060.7151.0000.7130.7040.7060.7140.7070.7070.7060.7080.7060.7070.7060.7070.7081.000
Main_Category0.7190.5850.7000.7040.7020.7190.7060.7090.7350.7880.2290.7030.7040.2490.7820.7750.7310.7131.0000.3040.7580.6000.7710.7150.7580.5790.8220.7060.5780.7320.7201.000
Main_Classification0.7370.5670.7420.7030.7070.7280.9640.7760.7270.7040.2260.9560.7020.1900.7260.7270.7090.7040.3041.0000.7120.5830.7030.6970.7110.5650.7380.7390.5610.7030.7231.000
National_Relevance_Present0.7070.7060.7060.7070.7060.7060.7060.7080.7080.7080.7010.7060.7070.6970.7080.7080.7070.7060.7580.7121.0000.7060.7070.7070.7070.7060.7170.7070.7090.7060.7081.000
Originality_and_Differentiation_Value0.7080.5840.7060.7060.7120.7090.7060.7120.7400.7090.5660.7060.7110.5680.7090.7110.7120.7140.6000.5830.7061.0000.7130.7060.7060.5980.7090.7070.5960.7590.7091.000
Personal_Identification_Present0.7080.7070.7060.7060.7070.7150.7060.7070.7060.7170.6950.7060.7060.6940.7130.7210.7090.7070.7710.7030.7070.7131.0000.7080.7070.7070.7150.7070.7090.7220.7121.000
Prohibition_Restriction_Present0.7080.7060.7090.7100.7060.7070.7060.7060.7070.7070.6980.7070.7060.7010.7230.7110.7180.7070.7150.6970.7070.7060.7081.0000.7060.7100.7060.7070.7070.7060.7071.000
Recognized_Brand_Present0.7120.7060.7070.7070.7080.7070.7070.7070.7070.7100.6990.7060.7070.6980.7070.7110.7060.7060.7580.7110.7070.7060.7070.7061.0000.7070.7130.7080.7110.7090.7061.000
Relevance_and_Timeliness_Value0.7090.5790.7060.7060.7060.7090.7060.7060.7060.7080.5590.7060.7060.5660.7070.7060.7060.7080.5790.5650.7060.5980.7070.7100.7071.0000.7070.7070.7260.7080.7071.000
Solution_Present0.7100.7070.7070.7060.7080.7110.7060.7060.7100.7070.6960.7070.7080.7050.7100.7860.7110.7060.8220.7380.7170.7090.7150.7060.7130.7071.0000.7070.7060.7080.7061.000
Starts_With_Number0.7060.7060.7070.7060.7080.7190.7070.7080.7070.7060.6900.7070.7070.6850.7060.7070.7060.7070.7060.7390.7070.7070.7070.7070.7080.7070.7071.0000.7060.7070.7061.000
Strategic_Keyword_Usage_Value0.7090.5770.7070.7060.7070.7090.7060.7060.7110.7090.5570.7060.7070.5510.7060.7060.7060.7060.5780.5610.7090.5960.7090.7070.7110.7260.7060.7061.0000.7080.7061.000
Surprise_Awe_Present0.7070.7060.7060.7070.7080.7070.7080.7070.7200.7070.7000.7070.7090.7020.7150.7070.7110.7070.7320.7030.7060.7590.7220.7060.7090.7080.7080.7070.7081.0000.7071.000
Temporal_Urgency_Present0.7060.7070.7060.7080.7060.7070.7080.7060.7070.7080.7050.7070.7070.6970.7210.7090.7080.7080.7200.7230.7080.7090.7120.7070.7060.7070.7060.7060.7060.7071.0001.000
Visibility1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2025-07-07T08:48:47.762419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-07T08:48:48.597445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TitleVisibilityCleaned_HeadlineMain_CategorySubcategory_1Subcategory_2Clarity_and_Conciseness_ValueClarity_and_Conciseness_CommentRelevance_and_Timeliness_ValueRelevance_and_Timeliness_CommentStrategic_Keyword_Usage_ValueStrategic_Keyword_Usage_CommentOriginality_and_Differentiation_ValueOriginality_and_Differentiation_CommentContains_NumbersContains_QuotesContains_Question_MarkContains_ColonContains_HyphenContains_Exclamation_MarkStarts_With_NumberEnds_With_Question_MarkLength_General_AssessmentLength_Number_of_CharactersEmphatic_Capitalization_UsageMain_ClassificationComment_Type_JustificationTemporal_Urgency_PresentTemporal_Urgency_WordsExclusivity_PresentExclusivity_WordsAuthority_PresentAuthority_WordsSolution_PresentSolution_WordsEconomic_Benefit_PresentEconomic_Benefit_WordsProhibition_Restriction_PresentProhibition_Restriction_WordsNational_Relevance_PresentNational_Relevance_WordsRecognized_Brand_PresentRecognized_Brand_WordsCuriosity_PresentCuriosity_EvidenceFear_Concern_PresentFear_Concern_EvidenceSurprise_Awe_PresentSurprise_Awe_EvidenceIndignation_Controversy_PresentIndignation_Controversy_EvidenceHope_Optimism_PresentHope_Optimism_EvidencePersonal_Identification_PresentPersonal_Identification_Evidence
0British jets intercept 15 Russian military aircraft10893901British jets intercept 15 Russian military aircraftNews_and_Current_EventsInternationalN/AHighThe headline is direct and clearly states the event without ambiguity.HighCovers a geopolitically relevant and timely topic concerning international military activities.HighUses clear, descriptive keywords like 'British jets', 'intercept', 'Russian military aircraft' which are highly relevant to the topic and discoverable.MediumWhile the event is specific, the phrasing is fairly standard for news reporting on military interceptions.YesNoNoNoNoNoNoNoAdequate46NoDeclarative SimpleIt is a straightforward statement of fact, reporting an event without any special framing.No[]No[]No[]No[]No[]No[]Yes["British","Russian"]No[]NoYesThe mention of 'intercept' and 'military aircraft' implies a potential threat or escalating tensions.NoNoNoNo
1New driveway rule change affecting every home in England has begun10439139New driveway rule change affecting every home in England has begunNews_and_Current_EventsGovernmentN/AHighThe headline is direct and easy to understand, clearly stating the what, who, and where.HighThe use of 'New' and 'has begun' creates a sense of immediacy and relevance for the target audience.HighUses specific and relevant keywords like 'driveway rule change' and 'England' that directly target a specific audience's potential interests.MediumWhile the topic is specific, the headline's structure is quite common for news alerts, lacking a highly unique angle.NoNoNoNoNoNoNoNoAdequate68NoDeclarative SimpleThe headline makes a direct statement about a new event or situation without asking a question or using sensational language.Yes["begun"]No[]No[]No[]No[]No[]Yes["England"]No[]YesThe headline creates an information gap by mentioning a 'new driveway rule change' without specifying what the change is, prompting the reader to click to find out.YesThe phrase 'affecting every home' can generate concern or fear among homeowners about potential negative consequences or obligations.NoNoNoYesDirectly appeals to residents of 'England' and specifically to those who are homeowners ('every home').
2The historic English city that is the best day trip within one hour of London10098169The historic English city that is the best day trip within one hour of LondonTravelDestinationsGuidesHighThe main message is very clear and easy to understand, directly stating the topic.HighDay trips and proximity to major cities like London are evergreen topics with high interest.HighUses highly relevant keywords like "historic English city," "best day trip," and "one hour of London" which are likely search terms.MediumWhile "best day trip" is a common superlative, the specific geographic and historical context provides some differentiation.YesNoNoNoNoNoNoNoAdequate69NoSuperlative ('best', 'worst')The headline uses the superlative "best" to describe the day trip experience.No[]No[]No[]Yes["best"]No[]No[]Yes["English","London"]No[]YesThe headline creates curiosity by promising the "best day trip" from London without immediately revealing the city.NoNoNoYesThe phrase "best day trip" evokes a sense of positive experience and an ideal outcome for readers seeking a trip.YesThe headline directly appeals to anyone interested in day trips from London, allowing them to easily identify with the topic.
3David Mitchell convicted for covering road with potatoes and silt10031250David Mitchell convicted for covering road with potatoes and siltNews_and_Current_EventsCrime & JudicialN/AHighThe headline clearly states who was convicted and for what unusual reason.LowThe event is localized and not tied to a major ongoing news trend, making its general relevance low.LowThe keywords are too specific to the event and do not align with common search queries.HighThe bizarre nature of the crime (covering a road with potatoes and silt) makes the headline highly original and memorable.NoNoNoNoNoNoNoNoAdequate65NoDeclarative SimpleThe headline states a fact directly without questions, quotes, or other complex structures.No[]No[]Yes["convicted"]No[]No[]No[]No[]No[]YesThe unusual act of 'covering road with potatoes and silt' creates a strong information gap and makes the reader want to know the backstory.NoYesThe combination of 'potatoes and silt' as a method for a crime is unexpected and bizarre.NoNoNo
4Say goodbye to parasols – Tesco is selling a smarter option that covers your entire outdoor space better9765281Say goodbye to parasols – Tesco is selling a smarter option that covers your entire outdoor space betterHome_and_LifestyleGardening_and_PlantsN/AHighThe main message is straightforward and easy to grasp.HighThe topic of outdoor space solutions is relevant, especially during warmer seasons, and appeals to practical home improvement interests.HighKeywords like "parasols", "Tesco", and "outdoor space" are relevant to the topic and target audience.HighThe headline positions the product as a "smarter option" and an improvement over traditional parasols, offering a unique angle.NoNoNoNoYesNoNoNoAdequate84NoComparative/SuperlativeThe headline compares a new "smarter option" to traditional "parasols", emphasizing its superior performance ("covers... better").No[]No[]No[]Yes["smarter option","better"]No[]No[]No[]Yes["Tesco"]YesThe phrase "smarter option that covers your entire outdoor space better" creates an information gap, prompting the reader to discover what this new solution is.NoNoNoYesThe headline offers a "smarter option" that performs "better," suggesting an improved solution to a common outdoor challenge, thereby instilling hope for a more effective product.YesThe phrase "Say goodbye to parasols" and reference to "your entire outdoor space" directly addresses the reader, implying a common problem they might face and offering a relevant solution.
5Travellers descend on local park as much-loved community event cancelled after overnight encampment9557677Travellers descend on local park as much-loved community event cancelled after overnight encampmentNews_and_Current_EventsCrime & JudicialN/AHighClearly states the main events and their consequence.HighRelates to a local community event and a timely disruption, highly relevant.HighUses specific and relevant keywords for local news interest.MediumThe event is specific, but the headline structure is common for news of this nature.NoNoNoNoYesNoNoNoAdequate99NoDeclarative SimpleThe headline directly informs the reader about an event and its cause without using questions, quotes, or urgency phrases.No[]No[]No[]No[]No[]No[]No[]No[]NoYescommunity event cancelled after overnight encampmentNoYesTravellers descend on local park as much-loved community event cancelled after overnight encampmentNoYeslocal park, community event
6Michael Schumacher, a tearful message from the clinic: "It was beautiful" – we will remember him forever9470424Michael Schumacher, a tearful message from the clinic: "It was beautiful" – we will remember him foreverSportsMotorsports (F1, MotoGP)N/AHighThe main message is easy to understand, conveying emotion and a sense of finality regarding Michael Schumacher.HighConnects with global interest in a highly recognized figure, evoking strong emotional engagement related to his legacy.HighUses "Michael Schumacher" as a strong primary keyword, alongside emotionally resonant phrases like "tearful message" and "remember him forever".HighThe direct quote and the emotional framing ("tearful message," "we will remember him forever") provide a unique and impactful angle.NoYesNoYesYesNoNoNoAdequate90NoDirect QuoteThe headline features a direct quote ("It was beautiful") and attributes it to Michael Schumacher from the clinic, making it a direct quote type.No[]No[]No[]No[]No[]No[]No[]No[]YesThe phrases "a tearful message from the clinic" and "It was beautiful" create an information gap, making readers curious about the context and meaning behind the message.YesThe mention of "clinic" and the implicit finality conveyed by "we will remember him forever" can evoke concern for his ongoing health condition and future.NoNoNoYesMany readers would identify with the feeling of remembering a beloved figure, fostering empathy and shared sentiment.
7Gardeners face £5,000 fine for mowing lawn on Saturday and Sunday9365831Gardeners face £5,000 fine for mowing lawn on Saturday and SundayHome_and_LifestyleGardening_and_PlantsN/AHighThe main message is very clear and direct, easy to understand.HighGardening and local regulations are topics of high general interest, especially regarding common weekend activities.HighKeywords like "Gardeners", "fine", "mowing lawn", "Saturday and Sunday" are highly relevant and likely to attract attention.MediumWhile the topic of fines is not new, the specific amount (£5,000) and the weekend restriction offer a somewhat unique angle.YesNoNoNoNoNoNoNoAdequate66NoDeclarative SimpleThe headline presents a direct statement of fact regarding a potential fine for an action.No[]No[]No[]No[]Yes["£5,000","fine"]Yes["fine for mowing lawn on Saturday and Sunday"]No[]No[]YesThe headline sparks curiosity about why gardeners face such a large fine for mowing on those specific days, and where this rule applies.YesThe mention of a "£5,000 fine" triggers concern for gardeners about potential penalties for common activities.YesA "£5,000 fine" for mowing a lawn on a weekend is surprisingly high and might evoke a sense of shock or disbelief.YesThe headline could provoke indignation, as many might view a fine for mowing a lawn on a weekend as an excessive or unreasonable restriction.NoYesThe headline directly appeals to "Gardeners" and anyone who mows their lawn, especially on weekends.
8Households urged to put sheet of A4 paper in fridge before Saturday9220647Households urged to put sheet of A4 paper in fridge before SaturdayHome_and_LifestyleHacksEnergy SavingHighThe instruction is clear and easy to understand.HighProvides a specific, time-sensitive instruction relevant to household management, likely energy saving.HighUses direct and relevant keywords like 'Households', 'fridge', 'A4 paper' that target the audience and topic.HighThe unusual instruction 'put sheet of A4 paper in fridge' makes the headline highly original and creates curiosity.YesNoNoNoNoNoNoNoAdequate64NoDeclarative SimpleThe headline presents a straightforward statement advising an action without questions or strong emotional punctuation.Yes["before","Saturday"]No[]No[]No[]No[]No[]No[]No[]YesThe unusual and unexplained action of putting 'A4 paper in fridge' before a specific deadline strongly triggers curiosity, making readers wonder why and what the benefit is.NoYesThe instruction itself is surprising due to its unconventional nature.NoNoYesDirectly addresses 'Households', making it highly relatable to a broad audience.
9Lando Norris leads tributes as popular F1 presenter retires after cancer diagnosis9175453Lando Norris leads tributes as popular F1 presenter retires after cancer diagnosisSportsMotorsports (F1, MotoGP)NewsHighThe message is direct and easy to understand, clearly stating the subject and the event.HighThe news involves a well-known personality in a popular sport (F1) and deals with a sensitive, high-interest human topic.HighUses highly relevant and searchable keywords like "Lando Norris", "F1", and "cancer diagnosis", which attract a specific and broad audience.LowIt's a standard and informative news headline formulation, lacking a unique or creative angle.NoNoNoNoNoNoNoNoAdequate88NoDeclarative SimpleThe headline states a fact or event directly without resorting to questions, superlatives, or other complex structures.No[]No[]No[]No[]No[]No[]No[]Yes["F1"]NoYesThe mention of "cancer diagnosis" generates concern and empathy for the presenter.NoNoNoYesAppeals to followers of F1 and fans of the personalities mentioned, creating a sense of community and shared feeling.
TitleVisibilityCleaned_HeadlineMain_CategorySubcategory_1Subcategory_2Clarity_and_Conciseness_ValueClarity_and_Conciseness_CommentRelevance_and_Timeliness_ValueRelevance_and_Timeliness_CommentStrategic_Keyword_Usage_ValueStrategic_Keyword_Usage_CommentOriginality_and_Differentiation_ValueOriginality_and_Differentiation_CommentContains_NumbersContains_QuotesContains_Question_MarkContains_ColonContains_HyphenContains_Exclamation_MarkStarts_With_NumberEnds_With_Question_MarkLength_General_AssessmentLength_Number_of_CharactersEmphatic_Capitalization_UsageMain_ClassificationComment_Type_JustificationTemporal_Urgency_PresentTemporal_Urgency_WordsExclusivity_PresentExclusivity_WordsAuthority_PresentAuthority_WordsSolution_PresentSolution_WordsEconomic_Benefit_PresentEconomic_Benefit_WordsProhibition_Restriction_PresentProhibition_Restriction_WordsNational_Relevance_PresentNational_Relevance_WordsRecognized_Brand_PresentRecognized_Brand_WordsCuriosity_PresentCuriosity_EvidenceFear_Concern_PresentFear_Concern_EvidenceSurprise_Awe_PresentSurprise_Awe_EvidenceIndignation_Controversy_PresentIndignation_Controversy_EvidenceHope_Optimism_PresentHope_Optimism_EvidencePersonal_Identification_PresentPersonal_Identification_Evidence
989270 home plans approved in Surrey after inspector concludes a nearby 'town' is actually a village320207270 home plans approved in Surrey after inspector concludes a nearby 'town' is actually a villageNews_and_Current_EventsEnvironmentN/AHighThe main message is easy and quick to understand, clearly stating the outcome and the reason.HighLocal development news is highly relevant to residents and communities in the area mentioned, addressing a specific local issue.HighUses direct and relevant keywords such as "home plans", "approved", "Surrey", "inspector", "town", and "village" that are likely to be searched for by a local audience.MediumWhile the structure is common, the specific detail about the "town" actually being a "village" provides a unique and potentially contentious angle that differentiates it.YesYesNoNoNoNoYesNoAdequate92NoDeclarative SimpleThe headline directly states a completed action and its contributing factors without using questions, commands, or dramatic framing.No[]No[]Yes["inspector","concludes"]No[]No[]No[]Yes["Surrey"]No[]YesThe distinction between 'town' and 'village' and its impact on the approval of 270 home plans creates an information gap, making readers curious about the significance of this reclassification.NoNoYesThe headline implies a dispute or reclassification ('town' is actually a 'village') that led to a significant decision (270 home plans approved), which could spark debate or indignation among those affected by or interested in local planning.NoYesResidents of Surrey or the specific 'town'/'village' mentioned would identify with the local impact of this decision, as it directly affects their community and living environment.
990‘It’s goodbye to French fishermen’: Macron under pressure as crucial UN ocean summit opens319502‘It’s goodbye to French fishermen’: Macron under pressure as crucial UN ocean summit opensNews_and_Current_EventsPoliticsEnvironmentHighThe main message regarding the situation of French fishermen and Macron's pressure in the context of the UN summit is clear and easy to understand.HighThe mention of a "crucial UN ocean summit opens" indicates high current relevance and timeliness.HighUses highly relevant keywords like "French fishermen", "Macron", and "UN ocean summit" which are specific to the topic and likely to appeal to the target audience.MediumWhile reporting on a current event, the direct quote and specific focus provide a distinct angle, though the phrasing is not exceptionally unique.NoYesNoYesNoNoNoNoAdequate91NoDirect Quote/AttributionThe headline starts with a direct quote, followed by a colon and the context, which attributes the sentiment or statement.No[]No[]No[]No[]No[]No[]Yes["French"]No[]YesThe phrase "It’s goodbye to French fishermen" creates an information gap, prompting the reader to seek the reasons behind it.YesThe potential loss for "French fishermen" and "Macron under pressure" evoke concern about negative consequences and difficulties.NoYesThe situation of fishermen and political pressure at a summit can easily spark debate and strong opinions, suggesting potential conflict or injustice.NoYesThe mention of "French fishermen" directly appeals to individuals within that group or those concerned with national industries and political outcomes.
991Ruth Langsford styles Broderie Anglaise blouse with trending turn-up jeans to create a breezy smart-casual look319337Ruth Langsford styles Broderie Anglaise blouse with trending turn-up jeans to create a breezy smart-casual lookFashionStyle TipsN/AHighThe main message about Ruth Langsford's outfit and its style is easy to understand.HighFashion is an evergreen topic, and the mention of "trending" adds timeliness. Celebrity endorsement increases relevance.HighUses relevant keywords like "Ruth Langsford", "Broderie Anglaise blouse", "trending turn-up jeans", and "smart-casual look" that are appealing for fashion-interested audiences.MediumWhile featuring a celebrity, the headline describes a common fashion topic in a straightforward manner, lacking a highly unique angle.NoNoNoNoYesNoNoNoAdequate108NoDeclarative SimpleThe headline makes a clear, straightforward statement about a fashion choice without posing a question or implying urgency.No[]No[]No[]No[]No[]No[]No[]Yes["Ruth Langsford"]YesThe mention of "trending turn-up jeans" and the description of "breezy smart-casual look" can pique curiosity about the specific style.NoNoNoNoYesReaders interested in fashion or Ruth Langsford's style might identify with the desire to replicate the described look.
992Scottish government to remove WhatsApp from phones318561Scottish government to remove WhatsApp from phonesNews_and_Current_EventsPoliticsGovernmentHighThe main message is direct and easy to understand.HighGovernment actions regarding widely used communication apps are highly relevant to the general public.HighKey terms like "Scottish government" and "WhatsApp" are present, attracting relevant audience interest.HighThe action described is significant and unusual, making the headline stand out.NoNoNoNoNoNoNoNoAdequate46NoDeclarative SimpleThe headline presents a clear and direct statement of an action to be taken by the Scottish government.No[]No[]Yes["government"]No[]No[]Yes["remove"]Yes["Scottish"]Yes["WhatsApp"]YesThe headline creates an information gap: why is the Scottish government removing WhatsApp and what are the implications?YesUsers of WhatsApp, especially those in Scotland, might feel concern over the potential loss of a communication tool or privacy implications.YesA government taking direct action to remove a widely used app from phones is an unexpected and notable event.YesSuch a government decision regarding a popular app is likely to stir debate and strong opinions among the public.NoYesThe headline directly impacts individuals who use WhatsApp in Scotland.
993All Virgin Media customers get big free TV upgrade with two new channels318074All Virgin Media customers get big free TV upgrade with two new channelsEntertainment_and_CultureSeries & PlatformsN/AHighThe main message is very clear and easy to understand, directly stating the benefit.HighHighly relevant for existing Virgin Media customers and those interested in TV services.HighUses direct and appealing keywords like "Virgin Media", "free TV upgrade", and "new channels".MediumWhile a common type of announcement, it is specific to a well-known brand and offers a clear benefit.YesNoNoNoNoNoNoNoAdequate59NoDeclarative SimpleThe headline simply states a fact about an upgrade for Virgin Media customers.No[]No[]No[]No[]Yes["free"]No[]No[]Yes["Virgin Media"]YesThe phrase "big free TV upgrade" and "two new channels" creates curiosity about the specific benefits and channel names.NoNoNoYesThe promise of a "free TV upgrade" and "new channels" evokes positive feelings and optimism for current customers.YesThe headline directly targets and identifies with "All Virgin Media customers", making it highly relevant to that group.
994Airport's £100m extension opens to passengers317796Airport's £100m extension opens to passengersTravelPlanning & TipsN/AHighThe main message is easy and quick to understand, with no ambiguity.HighAirport developments are highly relevant to current interests of travelers and local communities.MediumUses relevant keywords like "Airport", "extension", and "passengers", with "£100m" indicating significant investment.LowThis is a fairly standard news headline for an infrastructure project, lacking a unique angle.YesNoNoNoNoNoNoNoAdequate46NoDeclarative SimpleThe headline states a clear fact directly and simply without any special formatting or emotional appeals.No[]No[]No[]No[]Yes["£100m"]No[]No[]No[]NoNoNoNoYesThe opening of an "extension" suggests progress and improved services for travelers.YesThe word "passengers" directly appeals to individuals who travel or plan to travel.
995Warning issued after three dogs rescued from hot car in Aylesbury317685Warning issued after three dogs rescued from hot car in AylesburyPublic_SafetyAlerts & PreventionN/AHighThe message is clear and direct, detailing a warning, the rescue of dogs, the dangerous situation, and the location.HighAnimal welfare, particularly in dangerous situations like hot cars, is a consistently relevant and timely topic, especially during warmer periods.HighKeywords like "Warning", "dogs", "rescued", and "hot car" are highly relevant and likely to attract an audience interested in animal welfare and local news.MediumWhile "dogs in hot car" incidents are common, the specific detail of a "Warning issued" and the local context of "Aylesbury" offer a moderate level of differentiation.YesNoNoNoNoNoNoNoAdequate65NoDeclarative SimpleThe headline directly states a factual event without posing a question, quoting anyone, or implying urgency beyond the event itself.No[]No[]No[]No[]No[]No[]Yes["Aylesbury"]No[]NoYesThe phrase "Warning issued" implies a potential threat or danger, and "hot car" evokes concern for the dogs' well-being.NoYesThe act of leaving dogs in a "hot car" typically provokes indignation towards the responsible party.YesThe word "rescued" offers a positive resolution to the dangerous situation, instilling a sense of hope.No
996Britain's newest pedigree dog breed is rare, cuddly and curly-coated317591Britain's newest pedigree dog breed is rare, cuddly and curly-coatedHome_and_LifestylePetsN/AHighThe main message is clear and easy to understand.HighPets and new breeds are topics of high and evergreen interest.HighUses relevant keywords like "pedigree dog breed," "rare," "cuddly," and "curly-coated."MediumDescribes a new breed, which is somewhat unique, but the phrasing is largely descriptive.NoNoNoNoYesNoNoNoAdequate59NoDeclarative SimpleThe headline makes a straightforward factual statement about a new dog breed.No[]No[]No[]No[]No[]No[]Yes["Britain's"]No[]YesThe headline sparks curiosity about what this new, rare breed is and its characteristics.NoNoNoNoYesDog lovers and pet enthusiasts may identify with the appeal of a new, cuddly breed.
997British Gas, EDF, EON, OVO and Octopus customers urged to act before Monday316533British Gas, EDF, EON, OVO and Octopus customers urged to act before MondayFinance_and_BusinessCompanies & EntrepreneurshipN/AHighThe message is direct and easy to understand, clearly stating who needs to act and when.HighThe headline addresses major energy providers and includes a specific deadline, making it highly relevant and timely for a large segment of the population.HighThe names of the energy companies serve as strong keywords, directly targeting their customer base. "Customers" and "act before Monday" also enhance relevance and urgency.MediumWhile the structure is straightforward, the specific mention of multiple prominent energy providers and a clear deadline helps it stand out from more generic headlines.NoNoNoNoNoNoNoNoAdequate69NoUrgencyThe phrase "urged to act before Monday" explicitly conveys a time-sensitive call to action, emphasizing urgency.Yes["act","before Monday"]No[]No[]No[]No[]No[]Yes["British Gas","EDF","EON","OVO","Octopus"]Yes["British Gas","EDF","EON","OVO","Octopus"]NoYesThe urgent call to "act before Monday" implicitly suggests potential negative consequences or missed opportunities if the reader fails to comply.NoNoNoYesThe headline directly addresses "customers" of major energy companies, ensuring a high degree of personal relevance for a significant portion of the audience.
998Weekend walk: visit three stunning waterfalls and a village pub316132Weekend walk: visit three stunning waterfalls and a village pubTravelDestinationsGuidesHighThe main message is very clear and easy to understand.HighThis is an evergreen topic that appeals to individuals seeking leisure activities and scenic outings, especially on weekends.HighKeywords like "weekend walk," "waterfalls," and "village pub" are highly relevant and appealing to an audience interested in travel, nature, and leisure.MediumWhile the concept of weekend walks is common, the specific combination of "three stunning waterfalls and a village pub" offers a distinct and appealing angle.YesNoNoYesNoNoNoNoAdequate59NoDeclarative SimpleThe headline presents a straightforward statement, declaring an activity and its appealing elements without posing a question or making a strong claim of urgency, mystery, or comparison.No[]No[]No[]No[]No[]No[]No[]No[]YesThe mention of "three stunning waterfalls" and "a village pub" creates curiosity about the specific locations and what makes them stunning.NoYesThe word "stunning" aims to evoke a sense of awe or wonder at the described locations.NoYesSuggests a pleasant and enjoyable activity for the "weekend", promoting a positive outlook and the anticipation of a good experience.YesAppeals directly to anyone looking for a relaxing and scenic "weekend walk" and a traditional "pub" experience.